7 template<
typename FunctorType,
typename Scalar>
40 for (
j = 0;
j <
n; ++
j) {
50 fjac.col(
j) = (wa1-fvec)/h;
55 for (k = 0; k < msum; ++k) {
56 for (
j = k; (msum<0) ? (
j>
n): (
j<
n);
j += msum) {
58 h = eps *
abs(wa2[
j]);
65 for (
j = k; (msum<0) ? (
j>
n): (
j<
n);
j += msum) {
67 h = eps *
abs(wa2[
j]);
70 start = std::max<Index>(0,
j-mu);
72 fjac.col(
j).segment(start, length) = ( wa1.segment(start, length)-fvec.segment(start, length))/h;
Derived & setZero(Index rows, Index cols)
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)
: TensorContractionSycl.h, provides various tensor contraction kernel for SYCL backend
Eigen::AutoDiffScalar< EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Eigen::internal::remove_all_t< DerType >, typename Eigen::internal::traits< Eigen::internal::remove_all_t< DerType >>::Scalar, product) > sqrt(const Eigen::AutoDiffScalar< DerType > &x)
EIGEN_DEFAULT_DENSE_INDEX_TYPE Index
CleanedUpDerType< DerType >::type() min(const AutoDiffScalar< DerType > &x, const T &y)
CleanedUpDerType< DerType >::type() max(const AutoDiffScalar< DerType > &x, const T &y)
EIGEN_DEFAULT_DENSE_INDEX_TYPE DenseIndex
const Eigen::CwiseUnaryOp< Eigen::internal::scalar_abs_op< typename Derived::Scalar >, const Derived > abs(const Eigen::ArrayBase< Derived > &x)
const Eigen::CwiseUnaryOp< Eigen::internal::scalar_sqrt_op< typename Derived::Scalar >, const Derived > sqrt(const Eigen::ArrayBase< Derived > &x)
adouble abs(const adouble &x)