apply_jacobian_design apply_jacobian_design apply_jacobian_design apply_jacobian_design_transpose apply_jacobian_design_transpose apply_jacobian_design_transpose apply_linearization apply_linearization_transpose assemble_jacobian_state assemble_residual assemble_residual design_param DesignVec DesignVecGhosted get_da init IP_SSAHardavForwardProblem linearize_at m_design_param m_du_global m_du_local m_dzeta_local m_element m_element_index m_fixed_design_locations m_hardav m_J_state m_ksp m_reason m_rebuild_J_state m_stencil_width m_velocity_shared m_zeta set_design set_design_fixed_locations solution StateVec StateVec1 ~IP_SSAHardavForwardProblem

apply_linearization() void pism::inverse::IP_SSAHardavForwardProblem::apply_linearization ( array::Scalar &  dzeta, array::Vector &  du  ) virtual Applies the linearization of the forward map (i.e. the reduced gradient \(DF\) described in the class-level documentation.) As described previously, \[ Df = J_{\rm State}^{-1} J_{\rm Design}. \] Applying the linearization then involves the solution of a linear equation. The matrices \(J_{\rm State}\) and \(J_{\rm Design}\) both depend on the value of the design variable \(\zeta\) and the value of the corresponding state variable \(u=F(\zeta)\). These are established by first calling linearize_at. Parameters [in] dzeta Perturbation of the design variable [out] du Computed corresponding perturbation of the state variable; ghosts (if present) are updated. Definition at line 558 of file IP_SSAHardavForwardProblem.cc. References apply_jacobian_design(), assemble_jacobian_state(), pism::array::Array2D< T >::copy_from(), pism::RuntimeError::formatted(), m_du_global, m_J_state, m_ksp, pism::Component::m_log, m_rebuild_J_state, pism::stressbalance::ShallowStressBalance::m_velocity, PISM_CHK, PISM_ERROR_LOCATION, pism::array::Array::scale(), and pism::array::Array::vec().