minitensor::Vector<T, N>
gradient(minitensor::Vector<T, N> const& x)
{
// Firewalls.
minitensor::Index const dimension = x.get_dimension();
ALBANY_EXPECT(dimension == Base::DIMENSION);
// Variables that potentially have Albany::Traits sensitivity
// information need to be handled by the peel functor so that
// proper conversions take place.
T const K = peel<EvalT, T, N>()(K_);
T const smag = peel<EvalT, T, N>()(smag_);
T const mubar = peel<EvalT, T, N>()(mubar_);
T const Y = peel<EvalT, T, N>()(Y_);
// This is the actual computation of the gradient.
minitensor::Vector<T, N> r(dimension);
T const& X = x(0);
T const alpha = eqps_old_ + SQ23 * X;
T const H = K * alpha + sat_mod_ * (1.0 - std::exp(-sat_exp_ * alpha));
T const R = smag - (2.0 * mubar * X + SQ23 * (Y + H));
r(0) = R;
return r;
}
void
MiniTensorVector<T, N>::
axpy(T const alpha, Vector<T> const & x)
{
minitensor::Vector<T, N> const
xval = MTfromROL<T, N>(x);
auto const
dim = xval.get_dimension();
assert(vector_.get_dimension() == dim);
for (minitensor::Index i{0}; i < dim; ++i) {
vector_(i) += alpha * xval(i);
}
}