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hybrd_.c
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hybrd_.c
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/* hybrd.f -- translated by f2c (version 20020621).
You must link the resulting object file with the libraries:
-lf2c -lm (in that order)
*/
#include "minpack.h"
#include <math.h>
#include "minpackP.h"
__minpack_attr__
void __minpack_func__(hybrd)(__minpack_decl_fcn_nn__ const int *n, real *x, real *
fvec, const real *xtol, const int *maxfev, const int *ml, const int *mu,
const real *epsfcn, real *diag, const int *mode, const real *
factor, const int *nprint, int *info, int *nfev, real *
fjac, const int *ldfjac, real *r__, const int *lr, real *qtf,
real *wa1, real *wa2, real *wa3, real *wa4)
{
/* Table of constant values */
const int c__1 = 1;
const int c_false = FALSE_;
/* Initialized data */
#define p1 ((real).1)
#define p5 ((real).5)
#define p001 .001
#define p0001 1e-4
/* System generated locals */
int fjac_dim1, fjac_offset, i__1, i__2;
real d__1, d__2;
/* Local variables */
int i__, j, l, jm1, iwa[1];
real sum;
int sing;
int iter;
real temp;
int msum, iflag;
real delta;
int jeval;
int ncsuc;
real ratio;
real fnorm;
real pnorm, xnorm = 0, fnorm1;
int nslow1, nslow2;
int ncfail;
real actred, epsmch, prered;
/* ********** */
/* subroutine hybrd */
/* the purpose of hybrd is to find a zero of a system of */
/* n nonlinear functions in n variables by a modification */
/* of the powell hybrid method. the user must provide a */
/* subroutine which calculates the functions. the jacobian is */
/* then calculated by a forward-difference approximation. */
/* the subroutine statement is */
/* 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) */
/* where */
/* fcn is the name of the user-supplied subroutine which */
/* calculates the functions. fcn must be declared */
/* in an external statement in the user calling */
/* program, and should be written as follows. */
/* subroutine fcn(n,x,fvec,iflag) */
/* integer n,iflag */
/* double precision x(n),fvec(n) */
/* ---------- */
/* calculate the functions at x and */
/* return this vector in fvec. */
/* --------- */
/* return */
/* end */
/* the value of iflag should not be changed by fcn unless */
/* the user wants to terminate execution of hybrd. */
/* in this case set iflag to a negative integer. */
/* n is a positive integer input variable set to the number */
/* of functions and variables. */
/* x is an array of length n. on input x must contain */
/* an initial estimate of the solution vector. on output x */
/* contains the final estimate of the solution vector. */
/* fvec is an output array of length n which contains */
/* the functions evaluated at the output x. */
/* xtol is a nonnegative input variable. termination */
/* occurs when the relative error between two consecutive */
/* iterates is at most xtol. */
/* maxfev is a positive integer input variable. termination */
/* occurs when the number of calls to fcn is at least maxfev */
/* by the end of an iteration. */
/* ml is a nonnegative integer input variable which specifies */
/* the number of subdiagonals within the band of the */
/* jacobian matrix. if the jacobian is not banded, set */
/* ml to at least n - 1. */
/* mu is a nonnegative integer input variable which specifies */
/* the number of superdiagonals within the band of the */
/* jacobian matrix. if the jacobian is not banded, set */
/* mu to at least n - 1. */
/* epsfcn is an input variable used in determining a suitable */
/* step length for the forward-difference approximation. this */
/* approximation assumes that the relative errors in the */
/* functions are of the order of epsfcn. if epsfcn is less */
/* than the machine precision, it is assumed that the relative */
/* errors in the functions are of the order of the machine */
/* precision. */
/* diag is an array of length n. if mode = 1 (see */
/* below), diag is internally set. if mode = 2, diag */
/* must contain positive entries that serve as */
/* multiplicative scale factors for the variables. */
/* mode is an integer input variable. if mode = 1, the */
/* variables will be scaled internally. if mode = 2, */
/* the scaling is specified by the input diag. other */
/* values of mode are equivalent to mode = 1. */
/* factor is a positive input variable used in determining the */
/* initial step bound. this bound is set to the product of */
/* factor and the euclidean norm of diag*x if nonzero, or else */
/* to factor itself. in most cases factor should lie in the */
/* interval (.1,100.). 100. is a generally recommended value. */
/* nprint is an integer input variable that enables controlled */
/* printing of iterates if it is positive. in this case, */
/* fcn is called with iflag = 0 at the beginning of the first */
/* iteration and every nprint iterations thereafter and */
/* immediately prior to return, with x and fvec available */
/* for printing. if nprint is not positive, no special calls */
/* of fcn with iflag = 0 are made. */
/* info is an integer output variable. if the user has */
/* terminated execution, info is set to the (negative) */
/* value of iflag. see description of fcn. otherwise, */
/* info is set as follows. */
/* info = 0 improper input parameters. */
/* info = 1 relative error between two consecutive iterates */
/* is at most xtol. */
/* info = 2 number of calls to fcn has reached or exceeded */
/* maxfev. */
/* info = 3 xtol is too small. no further improvement in */
/* the approximate solution x is possible. */
/* info = 4 iteration is not making good progress, as */
/* measured by the improvement from the last */
/* five jacobian evaluations. */
/* info = 5 iteration is not making good progress, as */
/* measured by the improvement from the last */
/* ten iterations. */
/* nfev is an integer output variable set to the number of */
/* calls to fcn. */
/* fjac is an output n by n array which contains the */
/* orthogonal matrix q produced by the qr factorization */
/* of the final approximate jacobian. */
/* ldfjac is a positive integer input variable not less than n */
/* which specifies the leading dimension of the array fjac. */
/* r is an output array of length lr which contains the */
/* upper triangular matrix produced by the qr factorization */
/* of the final approximate jacobian, stored rowwise. */
/* lr is a positive integer input variable not less than */
/* (n*(n+1))/2. */
/* qtf is an output array of length n which contains */
/* the vector (q transpose)*fvec. */
/* wa1, wa2, wa3, and wa4 are work arrays of length n. */
/* subprograms called */
/* user-supplied ...... fcn */
/* minpack-supplied ... dogleg,dpmpar,enorm,fdjac1, */
/* qform,qrfac,r1mpyq,r1updt */
/* fortran-supplied ... dabs,dmax1,dmin1,min0,mod */
/* argonne national laboratory. minpack project. march 1980. */
/* burton s. garbow, kenneth e. hillstrom, jorge j. more */
/* ********** */
/* Parameter adjustments */
--wa4;
--wa3;
--wa2;
--wa1;
--qtf;
--diag;
--fvec;
--x;
fjac_dim1 = *ldfjac;
fjac_offset = 1 + fjac_dim1 * 1;
fjac -= fjac_offset;
--r__;
/* Function Body */
/* epsmch is the machine precision. */
epsmch = __minpack_func__(dpmpar)(&c__1);
*info = 0;
iflag = 0;
*nfev = 0;
/* check the input parameters for errors. */
if (*n <= 0 || *xtol < 0. || *maxfev <= 0 || *ml < 0 || *mu < 0 || *
factor <= 0. || *ldfjac < *n || *lr < *n * (*n + 1) / 2) {
goto L300;
}
if (*mode != 2) {
goto L20;
}
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
if (diag[j] <= 0.) {
goto L300;
}
/* L10: */
}
L20:
/* evaluate the function at the starting point */
/* and calculate its norm. */
iflag = 1;
fcn_nn(n, &x[1], &fvec[1], &iflag);
*nfev = 1;
if (iflag < 0) {
goto L300;
}
fnorm = __minpack_func__(enorm)(n, &fvec[1]);
/* determine the number of calls to fcn needed to compute */
/* the jacobian matrix. */
/* Computing MIN */
i__1 = *ml + *mu + 1;
msum = min(i__1,*n);
/* initialize iteration counter and monitors. */
iter = 1;
ncsuc = 0;
ncfail = 0;
nslow1 = 0;
nslow2 = 0;
/* beginning of the outer loop. */
L30:
jeval = TRUE_;
/* calculate the jacobian matrix. */
iflag = 2;
__minpack_func__(fdjac1)(__minpack_param_fcn_nn__ n, &x[1], &fvec[1], &fjac[fjac_offset], ldfjac, &iflag,
ml, mu, epsfcn, &wa1[1], &wa2[1]);
*nfev += msum;
if (iflag < 0) {
goto L300;
}
/* compute the qr factorization of the jacobian. */
__minpack_func__(qrfac)(n, n, &fjac[fjac_offset], ldfjac, &c_false, iwa, &c__1, &wa1[1], &
wa2[1], &wa3[1]);
/* on the first iteration and if mode is 1, scale according */
/* to the norms of the columns of the initial jacobian. */
if (iter != 1) {
goto L70;
}
if (*mode == 2) {
goto L50;
}
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
diag[j] = wa2[j];
if (wa2[j] == 0.) {
diag[j] = 1.;
}
/* L40: */
}
L50:
/* on the first iteration, calculate the norm of the scaled x */
/* and initialize the step bound delta. */
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
wa3[j] = diag[j] * x[j];
/* L60: */
}
xnorm = __minpack_func__(enorm)(n, &wa3[1]);
delta = *factor * xnorm;
if (delta == 0.) {
delta = *factor;
}
L70:
/* form (q transpose)*fvec and store in qtf. */
i__1 = *n;
for (i__ = 1; i__ <= i__1; ++i__) {
qtf[i__] = fvec[i__];
/* L80: */
}
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
if (fjac[j + j * fjac_dim1] == 0.) {
goto L110;
}
sum = 0.;
i__2 = *n;
for (i__ = j; i__ <= i__2; ++i__) {
sum += fjac[i__ + j * fjac_dim1] * qtf[i__];
/* L90: */
}
temp = -sum / fjac[j + j * fjac_dim1];
i__2 = *n;
for (i__ = j; i__ <= i__2; ++i__) {
qtf[i__] += fjac[i__ + j * fjac_dim1] * temp;
/* L100: */
}
L110:
/* L120: */
;
}
/* copy the triangular factor of the qr factorization into r. */
sing = FALSE_;
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
l = j;
jm1 = j - 1;
if (jm1 < 1) {
goto L140;
}
i__2 = jm1;
for (i__ = 1; i__ <= i__2; ++i__) {
r__[l] = fjac[i__ + j * fjac_dim1];
l = l + *n - i__;
/* L130: */
}
L140:
r__[l] = wa1[j];
if (wa1[j] == 0.) {
sing = TRUE_;
}
/* L150: */
}
/* accumulate the orthogonal factor in fjac. */
__minpack_func__(qform)(n, n, &fjac[fjac_offset], ldfjac, &wa1[1]);
/* rescale if necessary. */
if (*mode == 2) {
goto L170;
}
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
/* Computing MAX */
d__1 = diag[j], d__2 = wa2[j];
diag[j] = max(d__1,d__2);
/* L160: */
}
L170:
/* beginning of the inner loop. */
L180:
/* if requested, call fcn to enable printing of iterates. */
if (*nprint <= 0) {
goto L190;
}
iflag = 0;
if ((iter - 1) % *nprint == 0) {
fcn_nn(n, &x[1], &fvec[1], &iflag);
}
if (iflag < 0) {
goto L300;
}
L190:
/* determine the direction p. */
__minpack_func__(dogleg)(n, &r__[1], lr, &diag[1], &qtf[1], &delta, &wa1[1], &wa2[1], &wa3[
1]);
/* store the direction p and x + p. calculate the norm of p. */
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
wa1[j] = -wa1[j];
wa2[j] = x[j] + wa1[j];
wa3[j] = diag[j] * wa1[j];
/* L200: */
}
pnorm = __minpack_func__(enorm)(n, &wa3[1]);
/* on the first iteration, adjust the initial step bound. */
if (iter == 1) {
delta = min(delta,pnorm);
}
/* evaluate the function at x + p and calculate its norm. */
iflag = 1;
fcn_nn(n, &wa2[1], &wa4[1], &iflag);
++(*nfev);
if (iflag < 0) {
goto L300;
}
fnorm1 = __minpack_func__(enorm)(n, &wa4[1]);
/* compute the scaled actual reduction. */
actred = -1.;
if (fnorm1 < fnorm) {
/* Computing 2nd power */
d__1 = fnorm1 / fnorm;
actred = 1 - d__1 * d__1;
}
/* compute the scaled predicted reduction. */
l = 1;
i__1 = *n;
for (i__ = 1; i__ <= i__1; ++i__) {
sum = 0.;
i__2 = *n;
for (j = i__; j <= i__2; ++j) {
sum += r__[l] * wa1[j];
++l;
/* L210: */
}
wa3[i__] = qtf[i__] + sum;
/* L220: */
}
temp = __minpack_func__(enorm)(n, &wa3[1]);
prered = 0.;
if (temp < fnorm) {
/* Computing 2nd power */
d__1 = temp / fnorm;
prered = 1 - d__1 * d__1;
}
/* compute the ratio of the actual to the predicted */
/* reduction. */
ratio = 0.;
if (prered > 0.) {
ratio = actred / prered;
}
/* update the step bound. */
if (ratio >= p1) {
goto L230;
}
ncsuc = 0;
++ncfail;
delta = p5 * delta;
goto L240;
L230:
ncfail = 0;
++ncsuc;
if (ratio >= p5 || ncsuc > 1) {
/* Computing MAX */
d__1 = delta, d__2 = pnorm / p5;
delta = max(d__1,d__2);
}
if (fabs(ratio - 1) <= p1) {
delta = pnorm / p5;
}
L240:
/* test for successful iteration. */
if (ratio < p0001) {
goto L260;
}
/* successful iteration. update x, fvec, and their norms. */
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
x[j] = wa2[j];
wa2[j] = diag[j] * x[j];
fvec[j] = wa4[j];
/* L250: */
}
xnorm = __minpack_func__(enorm)(n, &wa2[1]);
fnorm = fnorm1;
++iter;
L260:
/* determine the progress of the iteration. */
++nslow1;
if (actred >= p001) {
nslow1 = 0;
}
if (jeval) {
++nslow2;
}
if (actred >= p1) {
nslow2 = 0;
}
/* test for convergence. */
if (delta <= *xtol * xnorm || fnorm == 0.) {
*info = 1;
}
if (*info != 0) {
goto L300;
}
/* tests for termination and stringent tolerances. */
if (*nfev >= *maxfev) {
*info = 2;
}
/* Computing MAX */
d__1 = p1 * delta;
if (p1 * max(d__1,pnorm) <= epsmch * xnorm) {
*info = 3;
}
if (nslow2 == 5) {
*info = 4;
}
if (nslow1 == 10) {
*info = 5;
}
if (*info != 0) {
goto L300;
}
/* criterion for recalculating jacobian approximation */
/* by forward differences. */
if (ncfail == 2) {
goto L290;
}
/* calculate the rank one modification to the jacobian */
/* and update qtf if necessary. */
i__1 = *n;
for (j = 1; j <= i__1; ++j) {
sum = 0.;
i__2 = *n;
for (i__ = 1; i__ <= i__2; ++i__) {
sum += fjac[i__ + j * fjac_dim1] * wa4[i__];
/* L270: */
}
wa2[j] = (sum - wa3[j]) / pnorm;
wa1[j] = diag[j] * (diag[j] * wa1[j] / pnorm);
if (ratio >= p0001) {
qtf[j] = sum;
}
/* L280: */
}
/* compute the qr factorization of the updated jacobian. */
__minpack_func__(r1updt)(n, n, &r__[1], lr, &wa1[1], &wa2[1], &wa3[1], &sing);
__minpack_func__(r1mpyq)(n, n, &fjac[fjac_offset], ldfjac, &wa2[1], &wa3[1]);
__minpack_func__(r1mpyq)(&c__1, n, &qtf[1], &c__1, &wa2[1], &wa3[1]);
/* end of the inner loop. */
jeval = FALSE_;
goto L180;
L290:
/* end of the outer loop. */
goto L30;
L300:
/* termination, either normal or user imposed. */
if (iflag < 0) {
*info = iflag;
}
iflag = 0;
if (*nprint > 0) {
fcn_nn(n, &x[1], &fvec[1], &iflag);
}
return;
/* last card of subroutine hybrd. */
} /* hybrd_ */