GenericAdaptiveVariationalSolver.h

Note

The documentation on this page was automatically extracted from the DOLFIN C++ code and may need to be edited or expanded.

class GenericAdaptiveVariationalSolver

Parent class(es)

An abstract class for goal-oriented adaptive solution of variational problems.

void solve(const double tol)

Solve such that the functional error is less than the given tolerance. Note that each call to solve is based on the leaf-node of the variational problem

Arguments
tol (double)
The error tolerance
boost::shared_ptr<const Function> solve_primal() = 0

Solve the primal problem. Must be overloaded in subclass.

Returns
Function
The solution to the primal problem
std::vector<boost::shared_ptr<const DirichletBC>> extract_bcs() const = 0

Extract the boundary conditions for the primal problem. Must be overloaded in subclass.

Returns
std::vector<DirichletBC>
The primal boundary conditions
double evaluate_goal(Form &M, boost::shared_ptr<const Function> u) const = 0

Evaluate the goal functional. Must be overloaded in subclass.

Arguments
M (Form)
The functional to be evaluated
u (Function)
The function of which to evaluate the functional
Returns
double
The value of M evaluated at u
void adapt_problem(boost::shared_ptr<const Mesh> mesh) = 0

Adapt the problem to other mesh. Must be overloaded in subclass.

Arguments
mesh (Mesh)
The other mesh
std::vector<boost::shared_ptr<Parameters>> adaptive_data() const

Return stored adaptive data

Returns
std::vector<Parameters>
The data stored in the adaptive loop
static Parameters default_parameters()

Default parameter values:

“max_iterations” (int) “max_dimension” (int) “plot_mesh” (bool) “save_data” (bool) “data_label” (std::string) “reference” (double) “marking_strategy” (std::string) “marking_fraction” (double)
void summary()

Present summary of all adaptive data and parameters

std::size_t num_dofs_primal() = 0

Return the number of degrees of freedom for primal problem

Returns
_std::size_t_
The number of degrees of freedom