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Reference documentation for deal.II version 9.3.3
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Reference documentation for deal.II version 9.3.3
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#include <deal.II/sundials/arkode.h>
Classes | |
| class | AdditionalData |
Public Member Functions | |
| ARKode (const AdditionalData &data=AdditionalData(), const MPI_Comm &mpi_comm=MPI_COMM_WORLD) | |
| ~ARKode () | |
| unsigned int | solve_ode (VectorType &solution) |
| unsigned int | solve_ode_incrementally (VectorType &solution, const double intermediate_time, const bool reset_solver=false) |
| void | reset (const double t, const double h, const VectorType &y) |
| void * | get_arkode_memory () const |
Public Attributes | |
| DEAL_II_DEPRECATED std::function< void(VectorType &)> | reinit_vector |
| std::function< int(const double t, const VectorType &y, VectorType &explicit_f)> | explicit_function |
| std::function< int(const double t, const VectorType &y, VectorType &res)> | implicit_function |
| std::function< int(const int convfail, const double t, const double gamma, const VectorType &ypred, const VectorType &fpred, bool &j_is_current)> | setup_jacobian |
| std::function< int(const double t, const double gamma, const VectorType &ycur, const VectorType &fcur, const VectorType &rhs, VectorType &dst)> | solve_jacobian_system |
| std::function< int(const double t)> | setup_mass |
| std::function< int(const VectorType &rhs, VectorType &dst)> | solve_mass_system |
| std::function< int(double t, const VectorType &v, VectorType &Mv)> | mass_times_vector |
| std::function< int(const double t)> | mass_times_setup |
| std::function< int(const VectorType &v, VectorType &Jv, double t, const VectorType &y, const VectorType &fy)> | jacobian_times_vector |
| std::function< int(realtype t, const VectorType &y, const VectorType &fy)> | jacobian_times_setup |
| LinearSolveFunction< VectorType > | solve_linearized_system |
| LinearSolveFunction< VectorType > | solve_mass |
| std::function< int(double t, const VectorType &y, const VectorType &fy, const VectorType &r, VectorType &z, double gamma, double tol, int lr)> | jacobian_preconditioner_solve |
| std::function< int(double t, const VectorType &y, const VectorType &fy, int jok, int &jcur, double gamma)> | jacobian_preconditioner_setup |
| std::function< int(double t, const VectorType &r, VectorType &z, double tol, int lr)> | mass_preconditioner_solve |
| std::function< int(double t)> | mass_preconditioner_setup |
| std::function< void(const double t, const VectorType &sol, const unsigned int step_number)> | output_step |
| std::function< bool(const double t, VectorType &sol)> | solver_should_restart |
| std::function< VectorType &()> | get_local_tolerances |
| std::function< void(void *arkode_mem)> | custom_setup |
Private Member Functions | |
| DeclException1 (ExcFunctionNotProvided, std::string,<< "Please provide an implementation for the function \""<< arg1<< "\"") | |
| int | do_evolve_time (VectorType &solution, ::DiscreteTime &time, const bool do_reset) |
| void | setup_system_solver (const VectorType &solution) |
| void | setup_mass_solver (const VectorType &solution) |
| void | set_functions_to_trigger_an_assert () |
Private Attributes | |
| AdditionalData | data |
| void * | arkode_mem |
| MPI_Comm | communicator |
| double | last_end_time |
| std::unique_ptr< internal::LinearSolverWrapper< VectorType > > | linear_solver |
| std::unique_ptr< internal::LinearSolverWrapper< VectorType > > | mass_solver |
Interface to SUNDIALS additive Runge-Kutta methods (ARKode).
The class ARKode is a wrapper to SUNDIALS variable-step, embedded, additive Runge-Kutta solver which is a general purpose solver for systems of ordinary differential equations characterized by the presence of both fast and slow dynamics.
Fast dynamics are treated implicitly, and slow dynamics are treated explicitly, using nested families of implicit and explicit Runge-Kutta solvers.
Citing directly from ARKode documentation:
ARKode solves ODE initial value problems (IVPs) in \(R^N\). These problems should be posed in explicit form as
\[ M\dot y = f_E(t, y) + f_I (t, y), \qquad y(t_0) = y_0. \]
Here, \(t\) is the independent variable (e.g. time), and the dependent variables are given by \(y \in R^N\), and we use notation \(\dot y\) to denote \(dy/dt\). \(M\) is a user-supplied nonsingular operator from \(R^N \to R^N\). This operator may depend on \(t\) but not on \(y\).
For standard systems of ordinary differential equations and for problems arising from the spatial semi-discretization of partial differential equations using finite difference or finite volume methods, \(M\) is typically the identity matrix, \(I\). For PDEs using a finite-element spatial semi-discretization \(M\) is typically a well-conditioned mass matrix.
The two right-hand side functions may be described as:
ARKode may be used to solve stiff, nonstiff and multi-rate problems. Roughly speaking, stiffness is characterized by the presence of at least one rapidly damped mode, whose time constant is small compared to the time scale of the solution itself. In the implicit/explicit (ImEx) splitting above, these stiff components should be included in the right-hand side function \(f_I (t, y)\).
For multi-rate problems, a user should provide both of the functions \(f_E\) and \(f_I\) that define the IVP system.
For nonstiff problems, only \(f_E\) should be provided, and \(f_I\) is assumed to be zero, i.e. the system reduces to the non-split IVP:
\[ M\dot y = f_E(t, y), \qquad y(t_0) = y_0. \]
In this scenario, the ARK methods reduce to classical explicit Runge-Kutta methods (ERK). For these classes of methods, ARKode allows orders of accuracy \(q = \{2, 3, 4, 5, 6, 8\}\), with embeddings of orders \(p = \{1, 2, 3, 4, 5, 7\}\). These default to the Heun-Euler-2-1-2, Bogacki-Shampine-4-2-3, Zonneveld-5-3-4, Cash-Karp-6-4-5, Verner-8-5-6 and Fehlberg-13-7-8 methods, respectively.
Finally, for stiff (linear or nonlinear) problems the user may provide only \(f_I\), implying that \(f_E = 0\), so that the system reduces to the non-split IVP
\[ M\dot y = f_I(t, y), \qquad y(t_0) = y_0. \]
Similarly to ERK methods, in this scenario the ARK methods reduce to classical diagonally-implicit Runge-Kutta methods (DIRK). For these classes of methods, ARKode allows orders of accuracy \(q = \{2, 3, 4, 5\}\), with embeddings of orders \(p = \{1, 2, 3, 4\}\). These default to the SDIRK-2-1-2, ARK-4-2-3 (implicit), SDIRK-5-3-4 and ARK-8-4-5 (implicit) methods, respectively.
For both DIRK and ARK methods, an implicit system of the form
\[ G(z_i) \dealcoloneq M z_i - h_n A^I_{i,i} f_I (t^I_{n,i}, z_i) - a_i = 0 \]
must be solved for each stage \(z_i , i = 1, \ldots, s\), where we have the data
\[ a_i \dealcoloneq M y_{n-1} + h_n \sum_{j=1}^{i-1} [ A^E_{i,j} f_E(t^E_{n,j}, z_j) + A^I_{i,j} f_I (t^I_{n,j}, z_j)] \]
for the ARK methods, or
\[ a_i \dealcoloneq M y_{n-1} + h_n \sum_{j=1}^{i-1} A^I_{i,j} f_I (t^I_{n,j}, z_j) \]
for the DIRK methods. Here \(A^I_{i,j}\) and \(A^E_{i,j}\) are the Butcher's tables for the chosen solver.
If \(f_I(t,y)\) depends nonlinearly on \(y\) then the systems above correspond to a nonlinear system of equations; if \(f_I (t, y)\) depends linearly on \(y\) then this is a linear system of equations. By specifying the flag implicit_function_is_linear, ARKode takes some shortcuts that allow a faster solution process.
For systems of either type, ARKode allows a choice of solution strategy. The default solver choice is a variant of Newton's method,
\[ z_i^{m+1} = z_i^m +\delta^{m+1}, \]
where \(m\) is the Newton index, and the Newton update \(\delta^{m+1}\) requires the solution of the linear Newton system
\[ N(z_i^m) \delta^{m+1} = -G(z_i^m), \]
where
\[ N \dealcoloneq M - \gamma J, \quad J \dealcoloneq \frac{\partial f_I}{\partial y}, \qquad \gamma\dealcoloneq h_n A^I_{i,i}. \]
As an alternate to Newton's method, ARKode may solve for each stage \(z_i ,i = 1, \ldots , s\) using an Anderson-accelerated fixed point iteration
\[ z_i^{m+1} = g(z_i^{m}), m=0,1,\ldots. \]
Unlike with Newton's method, this option does not require the solution of a linear system at each iteration, instead opting for solution of a low-dimensional least-squares solution to construct the nonlinear update.
Finally, if the user specifies implicit_function_is_linear, i.e., \(f_I(t, y)\) depends linearly on \(y\), and if the Newton-based nonlinear solver is chosen, then the system will be solved using only a single Newton iteration. Notice that in order for the Newton solver to be used, at least the jacobian_times_vector() function (or solve_jacobian_system() for SUNDIALS version > 4.0.0) should be supplied. If this function is not supplied, then only the fixed-point iteration will be supported, and the implicit_function_is_linear setting is ignored.
The optimal solver (Newton vs fixed-point) is highly problem-dependent. Since fixed-point solvers do not require the solution of any linear systems, each iteration may be significantly less costly than their Newton counterparts. However, this can come at the cost of slower convergence (or even divergence) in comparison with Newton-like methods. These fixed-point solvers do allow for user specification of the Anderson-accelerated subspace size, \(m_k\). While the required amount of solver memory grows proportionately to \(m_k N\), larger values of \(m_k\) may result in faster convergence.
This improvement may be significant even for "small" values, e.g. \(1 \leq m_k \leq 5\), and convergence may not improve (or even deteriorate) for larger values of \(m_k\). While ARKode uses a Newton-based iteration as its default solver due to its increased robustness on very stiff problems, it is highly recommended that users also consider the fixed-point solver for their cases when attempting a new problem.
For either the Newton or fixed-point solvers, it is well-known that both the efficiency and robustness of the algorithm intimately depends on the choice of a good initial guess. In ARKode, the initial guess for either nonlinear solution method is a predicted value \(z_i(0)\) that is computed explicitly from the previously-computed data (e.g. \(y_{n-2}, y_{n-1}\), and \(z_j\) where \(j < i\)). Additional information on the specific predictor algorithms implemented in ARKode is provided in ARKode documentation.
The user has to provide the implementation of at least one (or both) of the following std::functions:
If the mass matrix is different from the identity, the user should supply
If the use of a Newton method is desired, then the user should also supply
Only for SUNDIALS version > 4.0.0: A SUNDIALS default solver (SPGMR) is used to solve the linear systems. To use a custom linear solver for the mass matrix and/or Jacobian, set:
Only for SUNDIALS version > 4.0.0: To use a custom preconditioner with either a default or custom linear solver, set:
Also the following functions could be rewritten. By default they do nothing, or are not required.
To produce output at fixed steps, set the function
Any other custom settings of the ARKODE object can be specified in
To provide a simple example, consider the harmonic oscillator problem:
\[ \begin{split} u'' & = -k^2 u \\ u (0) & = 0 \\ u'(0) & = k \end{split} \]
We write it in terms of a first order ode:
\[ \begin{matrix} y_0' & = y_1 \\ y_1' & = - k^2 y_0 \end{matrix} \]
That is \(y' = A y\) where
\[ A \dealcoloneq \begin{matrix} 0 & 1 \\ -k^2 &0 \end{matrix} \]
and \(y(0)=(0, k)\).
The exact solution is \(y_0(t) = \sin(k t)\), \(y_1(t) = y_0'(t) = k \cos(k *t)\), \(y_1'(t) = -k^2 \sin(k t)\).
A minimal implementation, using only explicit RK methods, is given by the following code snippet:
| SUNDIALS::ARKode< VectorType >::ARKode | ( | const AdditionalData & | data = AdditionalData(), |
| const MPI_Comm & | mpi_comm = MPI_COMM_WORLD |
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| SUNDIALS::ARKode< VectorType >::~ARKode |
| unsigned int SUNDIALS::ARKode< VectorType >::solve_ode | ( | VectorType & | solution | ) |
| unsigned int SUNDIALS::ARKode< VectorType >::solve_ode_incrementally | ( | VectorType & | solution, |
| const double | intermediate_time, | ||
| const bool | reset_solver = false |
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Integrate the initial value problem. Compared to the function above, this function allows to specify an intermediate_time for the next solution. Repeated calls of this function must use monotonously increasing values for intermediate_time. The last solution state is saved internally along with the intermediate_time and will be reused as initial condition for the next call.
Users may find this function useful when integrating ARKode into an outer time loop of their own, especially when output_step() is too restrictive.
intermediate_time may be larger than AdditionalData::final_time, which is ignored by this function.| solution | The final solution. If the solver restarts, either because it is the first ever solve or the flag reset_solver is set, the vector is also used as initial condition. |
| intermediate_time | The time for the incremental solution step. Must be greater than the last time that was used in a previous call to this function. |
| reset_solver | Optional flag to recreate all internal objects which may be desirable for spatial adaptivity methods. If set to true, reset() is called before solving the ODE, which sets solution as initial condition. This will not reset the stored time from previous calls to this function. |
| void SUNDIALS::ARKode< VectorType >::reset | ( | const double | t, |
| const double | h, | ||
| const VectorType & | y | ||
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Clear internal memory and start with clean objects. This function is called when the simulation starts and when the user returns true to a call to solver_should_restart().
By default solver_should_restart() returns false. If the user needs to implement, for example, local adaptivity in space, he or she may assign a different function to solver_should_restart() that performs all mesh changes, transfers the solution to the new mesh, and returns true.
| t | The new starting time |
| h | The new starting time step |
| y | The new initial solution |
| void * SUNDIALS::ARKode< VectorType >::get_arkode_memory |
Provides user access to the internally used ARKODE memory.
This functionality is intended for users who wish to query additional information directly from the ARKODE integrator, refer to the ARKODE manual for the various ARKStepGet... functions. The ARKStepSet... functions should not be called since this might lead to conflicts with various settings that are performed by this ARKode object.
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Throw an exception when a function with the given name is not implemented.
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| DEAL_II_DEPRECATED std::function<void(VectorType &)> SUNDIALS::ARKode< VectorType >::reinit_vector |
A function object that was used to reinit the given vector. Setting this field does no longer have any effect and all auxiliary vectors are reinit-ed automatically based on the user-supplied vector in solve_ode().
| std::function< int(const double t, const VectorType &y, VectorType &explicit_f)> SUNDIALS::ARKode< VectorType >::explicit_function |
A function object that users may supply and that is intended to compute the explicit part of the IVP right hand side. Sets \(explicit_f = f_E(t, y)\).
At least one of explicit_function() or implicit_function() must be provided. According to which one is provided, explicit, implicit, or mixed RK methods are used.
This function should return:
| std::function<int(const double t, const VectorType &y, VectorType &res)> SUNDIALS::ARKode< VectorType >::implicit_function |
A function object that users may supply and that is intended to compute the implicit part of the IVP right hand side. Sets \(implicit_f = f_I(t, y)\).
At least one of explicit_function() or implicit_function() must be provided. According to which one is provided, explicit, implicit, or mixed RK methods are used.
This function should return:
| std::function<int(const int convfail, const double t, const double gamma, const VectorType &ypred, const VectorType &fpred, bool & j_is_current)> SUNDIALS::ARKode< VectorType >::setup_jacobian |
A function object that users may supply and that is intended to prepare the linear solver for subsequent calls to solve_jacobian_system().
Make sure that after a call to this function, we know how to compute solutions of systems \(A x = b\), where \(A\) is some approximation to the Newton matrix, \(M - \gamma \partial f_I/\partial y\). This function is optional. If the user does not provide it, then solve_jacobian_system() is assumed to also perform the setup internally.
The setup_jacobian() function may call a user-supplied function to compute needed data related to the Jacobian matrix. Alternatively, it may choose to retrieve and use stored values of this data. In either case, setup_jacobian() may also preprocess that data as needed for solve_jacobian_system(), which may involve calling a generic function (such as for LU factorization).
This data may be intended either for direct use (in a direct linear solver) or for use in a preconditioner (in a preconditioned iterative linear solver). The setup_jacobian() function is not called at every stage solve (or even every time step), but only as frequently as the solver determines that it is appropriate to perform the setup task. In this way, Jacobian-related data generated by setup_jacobian() is expected to be used over a number of time steps.
If the user uses a matrix based computation of the Jacobian, then this is the right place where an assembly routine shoulde be called to assemble both a matrix and a preconditioner for the Jacobian system. Subsequent calls (possibly more than one) to solve_jacobian_system() can assume that this function has been called at least once.
Notice that no assumption is made by this interface on what the user should do in this function. ARKode only assumes that after a call to setup_jacobian() it is possible to call solve_jacobian_system(), to obtain a solution \(x\) to the system \(J x = b\). If this function is not provided, then it is never called.
Arguments to the function are
| [in] | t | the current time |
| [in] | gamma | the current factor to use in the jacobian computation |
| [in] | ypred | is the predicted \(y\) vector for the current ARKode internal step |
| [in] | fpred | is the value of the implicit right-hand side at ypred, \(f_I (t_n, ypred)\). |
| [in] | convfail | Input flag used to indicate any problem that occurred during the solution of the nonlinear equation on the current time step for which the linear solver is being used. This flag can be used to help decide whether the Jacobian data kept by a linear solver needs to be updated or not. Its possible values are: |
| [out] | j_is_current | a boolean to be filled in by setup_jacobian(). The value should be set to true if the Jacobian data is current after the call, and should be set to false if its Jacobian data is not current. If setup_jacobian() calls for re-evaluation of Jacobian data (based on convfail and ARKode state data), then it should set j_is_current to true unconditionally, otherwise an infinite loop can result. |
This function should return:
| std::function<int(const double t, const double gamma, const VectorType &ycur, const VectorType &fcur, const VectorType &rhs, VectorType & dst)> SUNDIALS::ARKode< VectorType >::solve_jacobian_system |
A function object that users may supply and that is intended to solve the Jacobian linear system. This function will be called by ARKode (possibly several times) after setup_jacobian() has been called at least once. ARKode tries to do its best to call setup_jacobian() the minimum amount of times. If convergence can be achieved without updating the Jacobian, then ARKode does not call setup_jacobian() again. If, on the contrary, internal ARKode convergence tests fail, then ARKode calls again setup_jacobian() with updated vectors and coefficients so that successive calls to solve_jacobian_systems() lead to better convergence in the Newton process.
If you do not specify a solve_jacobian_system() function, then a fixed point iteration is used instead of a Newton method. Notice that this may not converge, or may converge very slowly.
The jacobian \(J\) should be (an approximation of) the system Jacobian
\[ J = M - \gamma \frac{\partial f_I}{\partial y} \]
evaluated at t, ycur. fcur is \(f_I(t,ycur)\).
A call to this function should store in dst the result of \(J^{-1}\) applied to src, i.e., J*dst = src. It is the users responsibility to set up proper solvers and preconditioners inside this function.
Arguments to the function are
| [in] | t | the current time |
| [in] | gamma | the current factor to use in the jacobian computation |
| [in] | ycur | is the current \(y\) vector for the current ARKode internal step |
| [in] | fcur | is the current value of the implicit right-hand side at ycur, \(f_I (t_n, ypred)\). |
This function should return:
| std::function<int(const double t)> SUNDIALS::ARKode< VectorType >::setup_mass |
A function object that users may supply and that is intended to set up the mass matrix. This function is called by ARKode any time a mass matrix update is required. The user should compute the mass matrix (or update all the variables that allow the application of the mass matrix). This function is called by ARKode once, before any call to solve_mass_system().
ARKode supports the case where the mass matrix may depend on time, but not the case where the mass matrix depends on the solution itself.
If the user does not provide a solve_mass_matrix() function, then the identity is used. If the setup_mass() function is not provided, then solve_mass_system() should do all the work by itself.
If the user uses a matrix based computation of the mass matrix, then this is the right place where an assembly routine shoulde be called to assemble both a matrix and a preconditioner. Subsequent calls (possibly more than one) to solve_mass_system() can assume that this function has been called at least once.
Notice that no assumption is made by this interface on what the user should do in this function. ARKode only assumes that after a call to setup_mass() it is possible to call solve_mass_system(), to obtain a solution \(x\) to the system \(M x = b\).
This function should return:
| std::function<int(const VectorType &rhs, VectorType &dst)> SUNDIALS::ARKode< VectorType >::solve_mass_system |
A function object that users may supply and that is intended to solve the mass matrix linear system. This function will be called by ARKode (possibly several times) after setup_mass() has been called at least once. ARKode tries to do its best to call setup_mass() the minimum amount of times.
A call to this function should store in dst the result of \(M^{-1}\) applied to src, i.e., M*dst = src. It is the users responsibility to set up proper solvers and preconditioners inside this function.
This function should return:
| std::function<int(double t, const VectorType &v, VectorType &Mv)> SUNDIALS::ARKode< VectorType >::mass_times_vector |
A function object that users may supply and that is intended to compute the product of the mass matrix with a given vector v. This function will be called by ARKode (possibly several times) after mass_times_setup() has been called at least once. ARKode tries to do its best to call mass_times_setup() the minimum amount of times.
A call to this function should store in Mv the result of \(M\) applied to v.
This function should return:
| std::function<int(const double t)> SUNDIALS::ARKode< VectorType >::mass_times_setup |
A function object that users may supply and that is intended to set up the mass matrix. This function is called by ARKode any time a mass matrix update is required. The user should compute the mass matrix (or update all the variables that allow the application of the mass matrix). This function is guaranteed to be called by ARKode at least once, before any call to mass_times_vector().
ARKode supports the case where the mass matrix may depend on time, but not the case where the mass matrix depends on the solution itself.
If the user does not provide a mass_times_vector() function, then the identity is used. If the mass_times_setup() function is not provided, then mass_times_vector() should do all the work by itself.
If the user uses a matrix-based computation of the mass matrix, then this is the right place where an assembly routine should be called to assemble the matrix. Subsequent calls (possibly more than one) to mass_times_vector() can assume that this function has been called at least once.
| t | The current evaluation time |
This function should return:
| std::function<int(const VectorType &v, VectorType & Jv, double t, const VectorType &y, const VectorType &fy)> SUNDIALS::ARKode< VectorType >::jacobian_times_vector |
A function object that users may supply and that is intended to compute the product of the Jacobian matrix with a given vector v. The Jacobian here refers to \(J=\frac{\partial f_I}{\partial y}\), i.e., the Jacobian of the user-specified implicit_function.
A call to this function should store in Jv the result of \(J\) applied to v.
Arguments to the function are
| [in] | v | The vector to be multiplied by the Jacobian |
| [out] | Jv | The vector to be filled with the product J*v |
| [in] | t | The current time |
| [in] | y | The current \(y\) vector for the current ARKode internal step |
| [in] | fy | The current value of the implicit right-hand side at y, \(f_I (t_n, y)\). |
This function should return:
| std::function<int(realtype t, const VectorType &y, const VectorType &fy)> SUNDIALS::ARKode< VectorType >::jacobian_times_setup |
A function object that users may supply and that is intended to set up all data necessary for the application of jacobian_times_setup(). This function is called by ARKode any time a Jacobian update is required. The user should compute the Jacobian (or update all the variables that allow the application of Jacobian). This function is guaranteed to be called by ARKode at least once, before any call to jacobian_times_vector().
If the jacobian_times_setup() function is not provided, then jacobian_times_vector() should do all the work by itself.
If the user uses a matrix based computation of the Jacobian, then this is the right place where an assembly routine should be called to assemble the matrix. Subsequent calls (possibly more than one) to jacobian_times_vector() can assume that this function has been called at least once.
| t | The current time |
| y | The current ARKode internal solution vector \(y\) |
| fy | The implicit right-hand side function evaluated at the current time \(t\) and state \(y\), i.e., \(f_I(y,t)\) |
This function should return:
| LinearSolveFunction<VectorType> SUNDIALS::ARKode< VectorType >::solve_linearized_system |
A LinearSolveFunction object that users may supply and that is intended to solve the linearized system \(Ax=b\), where \(A = M-\gamma J\) is the Jacobian of the nonlinear residual. The application of the mass matrix \(M\) and Jacobian \(J\) are known through the functions mass_times_vector() and jacobian_times_vector() and \(\gamma\) is a factor provided by SUNDIALS. The matrix-vector product \(Ax\) is encoded in the supplied SundialsOperator. If a preconditioner was set through jacobian_preconditioner_solve(), it is encoded in the SundialsPreconditioner. If no preconditioner was supplied this way, the preconditioner is the identity matrix, i.e., no preconditioner. The user is free to use a custom preconditioner in this function object that is not supplied through SUNDIALS.
If you do not specify a solve_linearized_system() function, then a SUNDIALS packaged SPGMR solver with default settings is used.
For more details on the function type refer to LinearSolveFunction.
| LinearSolveFunction<VectorType> SUNDIALS::ARKode< VectorType >::solve_mass |
A LinearSolveFunction object that users may supply and that is intended to solve the mass system \(Mx=b\). The matrix-vector product \(Mx\) is encoded in the supplied SundialsOperator. If a preconditioner was set through mass_preconditioner_solve(), it is encoded in the SundialsPreconditioner. If no preconditioner was supplied this way, the preconditioner is the identity matrix, i.e., no preconditioner. The user is free to use a custom preconditioner in this function object that is not supplied through SUNDIALS.
The user must specify this function if a non-identity mass matrix is used and applied in mass_times_vector().
For more details on the function type refer to LinearSolveFunction.
| std::function<int(double t, const VectorType &y, const VectorType &fy, const VectorType &r, VectorType & z, double gamma, double tol, int lr)> SUNDIALS::ARKode< VectorType >::jacobian_preconditioner_solve |
A function object that users may supply to either pass a preconditioner to a SUNDIALS built-in solver or to apply a custom preconditioner within the user's own linear solve specified in solve_linearized_system().
This function should compute the solution to the preconditioner equation \(Pz=r\) and store it in z. In this equation \(P\) should approximate the Jacobian \(M-\gamma J\) of the nonlinear system.
| [in] | t | The current time |
| [in] | y | The current \(y\) vector for the current ARKode internal step |
| [in] | fy | The current value of the implicit right-hand side at y, \(f_I (t_n, y)\). |
| [in] | r | The right-hand side of the preconditioner equation |
| [out] | z | The solution of applying the preconditioner, i.e., solving \(Pz=r\) |
| [in] | gamma | The value \(\gamma\) in the preconditioner equation |
| [in] | tol | The tolerance up to which the system should be solved |
| [in] | lr | An input flag indicating whether the preconditioner solve is to use the left preconditioner (lr = 1) or the right preconditioner (lr = 2). Only relevant if used with a SUNDIALS packaged solver. If used with a custom solve_mass() function this will be set to zero. |
This function should return:
| std::function<int(double t, const VectorType &y, const VectorType &fy, int jok, int & jcur, double gamma)> SUNDIALS::ARKode< VectorType >::jacobian_preconditioner_setup |
A function object that users may supply to set up a preconditioner specified in jacobian_preconditioner_solve().
This function should prepare the solution of the preconditioner equation \(Pz=r\). In this equation \(P\) should approximate the Jacobian \(M-\gamma J\) of the nonlinear system.
If the jacobian_preconditioner_setup() function is not provided, then jacobian_preconditioner_solve() should do all the work by itself.
| [in] | t | The current time |
| [in] | y | The current \(y\) vector for the current ARKode internal step |
| [in] | fy | The current value of the implicit right-hand side at y, \(f_I (t_n, y)\). |
| [in] | jok | An input flag indicating whether the Jacobian-related data needs to be updated. The jok argument provides for the reuse of Jacobian data in the preconditioner solve function. When jok = SUNFALSE, the Jacobian-related data should be recomputed from scratch. When jok = SUNTRUE the Jacobian data, if saved from the previous call to this function, can be reused (with the current value of gamma). A call with jok = SUNTRUE can only occur after a call with jok = SUNFALSE. |
| [out] | jcur | On output this should be set to SUNTRUE if Jacobian data was recomputed, or set to SUNFALSE if Jacobian data was not recomputed, but saved data was still reused. |
| [in] | gamma | The value \(\gamma\) in \(M-\gamma J\). The preconditioner should approximate the inverse of this matrix. |
This function should return:
| std::function< int(double t, const VectorType &r, VectorType &z, double tol, int lr)> SUNDIALS::ARKode< VectorType >::mass_preconditioner_solve |
A function object that users may supply to either pass a preconditioner to a SUNDIALS built-in solver or to apply a custom preconditioner within the user's own linear solve specified in solve_mass().
This function should compute the solution to the preconditioner equation \(Pz=r\) and store it in z. In this equation \(P\) should approximate the mass matrix \(M\).
| [in] | t | The current time |
| [in] | r | The right-hand side of the preconditioner equation |
| [out] | z | The solution of applying the preconditioner, i.e., solving \(Pz=r\) |
| [in] | gamma | The value \(\gamma\) in the preconditioner equation |
| [in] | tol | The tolerance up to which the system should be solved |
| [in] | lr | An input flag indicating whether the preconditioner solve is to use the left preconditioner (lr = 1) or the right preconditioner (lr = 2). Only relevant if used with a SUNDIALS packaged solver. If used with a custom solve_mass() function this will be set to zero. |
This function should return:
| std::function<int(double t)> SUNDIALS::ARKode< VectorType >::mass_preconditioner_setup |
A function object that users may supply to set up a preconditioner specified in mass_preconditioner_solve().
This function should prepare the solution of the preconditioner equation \(Pz=r\). In this equation \(P\) should approximate the mass matrix \(M\).
If the mass_preconditioner_setup() function is not provided, then mass_preconditioner_solve() should do all the work by itself.
| [in] | t | The current time |
This function should return:
| std::function<void(const double t, const VectorType & sol, const unsigned int step_number)> SUNDIALS::ARKode< VectorType >::output_step |
A function object that users may supply and that is intended to postprocess the solution. This function is called by ARKode at fixed time increments (every output_period seconds), and it is passed a polynomial interpolation of the solution, computed using the current ARK order and the (internally stored) previously computed solution steps.
output_period step, and therefore calls this function consecutively several times by simply performing all intermediate interpolations. There is no relationship between how many times this function is called and how many time steps have actually been computed. | std::function<bool(const double t, VectorType &sol)> SUNDIALS::ARKode< VectorType >::solver_should_restart |
A function object that users may supply and that is intended to evaluate whether the solver should be restarted (for example because the number of degrees of freedom has changed).
This function is supposed to perform all operations that are necessary in sol to make sure that the resulting vectors are consistent, and of the correct final size.
For example, one may decide that a local refinement is necessary at time t. This function should then return true, and change the dimension of sol to reflect the new dimension. Since ARKode does not know about the new dimension, an internal reset is necessary.
The default implementation simply returns false, i.e., no restart is performed during the evolution.
| std::function<VectorType &()> SUNDIALS::ARKode< VectorType >::get_local_tolerances |
| std::function<void(void *arkode_mem)> SUNDIALS::ARKode< VectorType >::custom_setup |
A function object that users may supply and which is intended to perform custom settings on the supplied arkode_mem object. Refer to the SUNDIALS documentation for valid options.
For instance, the following code attaches two files for diagnostic and error output of the internal ARKODE implementation:
| arkode_mem | pointer to the ARKODE memory block which can be used for custom calls to ARKStepSet... methods. |
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The final time in the last call to solve_ode().
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1.9.4