namespace Eigen {
namespace internal {
template <typename FunctorType, typename Scalar>
DenseIndex fdjac1(
const FunctorType &Functor,
Matrix< Scalar, Dynamic, 1 > &x,
Matrix< Scalar, Dynamic, 1 > &fvec,
Matrix< Scalar, Dynamic, Dynamic > &fjac,
DenseIndex ml, DenseIndex mu,
Scalar epsfcn)
{
using std::sqrt;
using std::abs;
typedef DenseIndex Index;
/* Local variables */
Scalar h;
Index j, k;
Scalar eps, temp;
Index msum;
int iflag;
Index start, length;
/* Function Body */
const Scalar epsmch = NumTraits<Scalar>::epsilon();
const Index n = x.size();
eigen_assert(fvec.size()==n);
Matrix< Scalar, Dynamic, 1 > wa1(n);
Matrix< Scalar, Dynamic, 1 > wa2(n);
eps = sqrt((std::max)(epsfcn,epsmch));
msum = ml + mu + 1 ;
if (msum >= n) {
/* computation of dense approximate jacobian. */
for (j = 0 ; j < n; ++j) {
temp = x[j];
h = eps * abs(temp);
if (h == 0 .)
h = eps;
x[j] = temp + h;
iflag = Functor(x, wa1);
if (iflag < 0 )
return iflag;
x[j] = temp;
fjac.col(j) = (wa1-fvec)/h;
}
}else {
/* computation of banded approximate jacobian. */
for (k = 0 ; k < msum; ++k) {
for (j = k; (msum<0 ) ? (j>n): (j<n); j += msum) {
wa2[j] = x[j];
h = eps * abs(wa2[j]);
if (h == 0 .) h = eps;
x[j] = wa2[j] + h;
}
iflag = Functor(x, wa1);
if (iflag < 0 )
return iflag;
for (j = k; (msum<0 ) ? (j>n): (j<n); j += msum) {
x[j] = wa2[j];
h = eps * abs(wa2[j]);
if (h == 0 .) h = eps;
fjac.col(j).setZero();
start = std::max<Index>(0 ,j-mu);
length = (std::min)(n-1 , j+ml) - start + 1 ;
fjac.col(j).segment(start, length) = ( wa1.segment(start, length)-fvec.segment(start, length))/h;
}
}
}
return 0 ;
}
} // end namespace internal
} // end namespace Eigen
Messung V0.5 in Prozent C=88 H=88 G=87
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(vorverarbeitet am 2026-06-06)
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