gradientAt IPLogRelativeFunctional m_eps m_normalization m_u_observed m_weights normalize valueAt ~IPLogRelativeFunctional

gradientAt() void pism::inverse::IPLogRelativeFunctional::gradientAt ( array::Vector &  x, array::Vector &  gradient  ) virtual Computes the gradient of the functional at the vector x. On an \(m\times n\) Grid, an array::Array \(x\) with \(d\) degrees of freedom will be \(d m n\)-dimensional with components \(x_i\). The gradient computed here is the vector of directional derivatives \(\nabla J\) of the functional \(J\) with respect to \(x\). Concretely, the \(i^{\rm th}\) component of \(\nabla J\) is \[ \nabla J_i = \frac{\partial}{\partial x_i} J(x). \] This vector is returned as gradient. Implements pism::inverse::IPFunctional< array::Vector >. Definition at line 93 of file IPLogRelativeFunctional.cc. References pism::array::Array2D< T >::add(), m_eps, pism::inverse::IPFunctional< array::Vector >::m_grid, m_normalization, m_u_observed, m_weights, pism::array::Array::set(), pism::Vector2d::u, and pism::Vector2d::v.