Files
eigen/unsupported/Eigen/src/NonLinear/hybrd.h
Thomas Capricelli 5e8dee7a19 eigenize dogleg()
2009-08-23 21:39:47 +02:00

388 lines
8.8 KiB
C++

template<typename Functor, typename Scalar>
int ei_hybrd(
Matrix< Scalar, Dynamic, 1 > &x,
Matrix< Scalar, Dynamic, 1 > &fvec,
int &nfev,
Matrix< Scalar, Dynamic, Dynamic > &fjac,
Matrix< Scalar, Dynamic, 1 > &R,
Matrix< Scalar, Dynamic, 1 > &qtf,
Matrix< Scalar, Dynamic, 1 > &diag,
int mode=1,
int nb_of_subdiagonals = -1,
int nb_of_superdiagonals = -1,
int maxfev = 2000,
Scalar factor = Scalar(100.),
Scalar xtol = ei_sqrt(epsilon<Scalar>()),
Scalar epsfcn = Scalar(0.),
int nprint=0
)
{
const int n = x.size();
int lr = (n*(n+1))/2;
Matrix< Scalar, Dynamic, 1 > wa1(n), wa2(n), wa3(n), wa4(n);
if (nb_of_subdiagonals<0) nb_of_subdiagonals = n-1;
if (nb_of_superdiagonals<0) nb_of_superdiagonals = n-1;
fvec.resize(n);
qtf.resize(n);
R.resize(lr);
fjac.resize(n, n);
/* Local variables */
int i, j, l, iwa[1];
Scalar sum;
int sing;
int iter;
Scalar temp;
int msum, iflag;
Scalar delta;
int jeval;
int ncsuc;
Scalar ratio;
Scalar fnorm;
Scalar pnorm, xnorm, fnorm1;
int nslow1, nslow2;
int ncfail;
Scalar actred, prered;
int info;
/* Function Body */
info = 0;
iflag = 0;
nfev = 0;
/* check the input parameters for errors. */
if (n <= 0 || xtol < 0. || maxfev <= 0 || nb_of_subdiagonals < 0 || nb_of_superdiagonals < 0 ||
factor <= 0. || lr < n * (n + 1) / 2) {
goto L300;
}
if (mode == 2)
for (j = 0; j < n; ++j)
if (diag[j] <= 0.) goto L300;
/* evaluate the function at the starting point */
/* and calculate its norm. */
iflag = Functor::f(x, fvec);
nfev = 1;
if (iflag < 0) {
goto L300;
}
fnorm = fvec.stableNorm();
/* determine the number of calls to fcn needed to compute */
/* the jacobian matrix. */
/* Computing MIN */
msum = std::min(nb_of_subdiagonals + nb_of_superdiagonals + 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 = ei_fdjac1<Functor,Scalar>(x, fvec, fjac,
nb_of_subdiagonals, nb_of_superdiagonals, epsfcn, wa1, wa2);
nfev += msum;
if (iflag < 0) {
goto L300;
}
/* compute the qr factorization of the jacobian. */
ei_qrfac<Scalar>(n, n, fjac.data(), fjac.rows(), false, iwa, 1, wa1.data(), wa2.data(), wa3.data());
/* 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;
}
for (j = 0; j < n; ++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. */
wa3 = diag.cwise() * x;
xnorm = wa3.stableNorm();
delta = factor * xnorm;
if (delta == 0.) {
delta = factor;
}
L70:
/* form (q transpose)*fvec and store in qtf. */
qtf = fvec;
for (j = 0; j < n; ++j) {
if (fjac(j,j) == 0.) {
goto L110;
}
sum = 0.;
for (i = j; i < n; ++i) {
sum += fjac(i,j) * qtf[i];
/* L90: */
}
temp = -sum / fjac(j,j);
for (i = j; i < n; ++i) {
qtf[i] += fjac(i,j) * temp;
/* L100: */
}
L110:
/* L120: */
;
}
/* copy the triangular factor of the qr factorization into r. */
sing = false;
for (j = 0; j < n; ++j) {
l = j;
if (j) {
for (i = 0; i < j; ++i) {
R[l] = fjac(i,j);
l = l + n - i -1;
/* L130: */
}
}
R[l] = wa1[j];
if (wa1[j] == 0.) {
sing = true;
}
/* L150: */
}
/* accumulate the orthogonal factor in fjac. */
ei_qform<Scalar>(n, n, fjac.data(), fjac.rows(), wa1.data());
/* rescale if necessary. */
if (mode == 2) {
goto L170;
}
/* Computing MAX */
diag = diag.cwise().max(wa2);
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) {
iflag = Functor::debug(x, fvec);
}
if (iflag < 0) {
goto L300;
}
L190:
/* determine the direction p. */
ei_dogleg<Scalar>(R, diag, qtf, delta, wa1);
/* store the direction p and x + p. calculate the norm of p. */
wa1 = -wa1;
wa2 = x + wa1;
wa3 = diag.cwise() * wa1;
pnorm = wa3.stableNorm();
/* on the first iteration, adjust the initial step bound. */
if (iter == 1) {
delta = std::min(delta,pnorm);
}
/* evaluate the function at x + p and calculate its norm. */
iflag = Functor::f(wa2, wa4);
++nfev;
if (iflag < 0) {
goto L300;
}
fnorm1 = wa4.stableNorm();
/* compute the scaled actual reduction. */
actred = -1.;
if (fnorm1 < fnorm) /* Computing 2nd power */
actred = 1. - ei_abs2(fnorm1 / fnorm);
/* compute the scaled predicted reduction. */
l = 0;
for (i = 0; i < n; ++i) {
sum = 0.;
for (j = i; j < n; ++j) {
sum += R[l] * wa1[j];
++l;
/* L210: */
}
wa3[i] = qtf[i] + sum;
/* L220: */
}
temp = wa3.stableNorm();
prered = 0.;
if (temp < fnorm) /* Computing 2nd power */
prered = 1. - ei_abs2(temp / fnorm);
/* compute the ratio of the actual to the predicted */
/* reduction. */
ratio = 0.;
if (prered > 0.) {
ratio = actred / prered;
}
/* update the step bound. */
if (ratio >= Scalar(.1)) {
goto L230;
}
ncsuc = 0;
++ncfail;
delta = Scalar(.5) * delta;
goto L240;
L230:
ncfail = 0;
++ncsuc;
if (ratio >= Scalar(.5) || ncsuc > 1) /* Computing MAX */
delta = std::max(delta, pnorm / Scalar(.5));
if (ei_abs(ratio - 1.) <= Scalar(.1)) {
delta = pnorm / Scalar(.5);
}
L240:
/* test for successful iteration. */
if (ratio < Scalar(1e-4)) {
goto L260;
}
/* successful iteration. update x, fvec, and their norms. */
x = wa2;
wa2 = diag.cwise() * x;
fvec = wa4;
temp = wa2.stableNorm();
fnorm = fnorm1;
++iter;
L260:
/* determine the progress of the iteration. */
++nslow1;
if (actred >= Scalar(.001)) {
nslow1 = 0;
}
if (jeval) {
++nslow2;
}
if (actred >= Scalar(.1)) {
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 */
if (Scalar(.1) * std::max(Scalar(.1) * delta, pnorm) <= epsilon<Scalar>() * 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. */
for (j = 0; j < n; ++j) {
sum = wa4.dot(fjac.col(j));
wa2[j] = (sum - wa3[j]) / pnorm;
wa1[j] = diag[j] * (diag[j] * wa1[j] / pnorm);
if (ratio >= Scalar(1e-4))
qtf[j] = sum;
}
/* compute the qr factorization of the updated jacobian. */
ei_r1updt<Scalar>(n, n, R.data(), lr, wa1.data(), wa2.data(), wa3.data(), &sing);
ei_r1mpyq<Scalar>(n, n, fjac.data(), fjac.rows(), wa2.data(), wa3.data());
ei_r1mpyq<Scalar>(1, n, qtf.data(), 1, wa2.data(), wa3.data());
/* 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;
}
if (nprint > 0) {
iflag = Functor::debug(x, fvec);
}
return info;
/* last card of subroutine hybrd. */
} /* hybrd_ */