InversePowerMethod

Inverse power method for eigenvalue problems.

Overview

Eigenvalue executioners such as this one intend on solving the eigenvalue problem described by:

var element = document.getElementById("moose-equation-4f4e7956-cd05-4648-9d0e-7a7bcd33fe9d");katex.render("Ax = \\frac{1}{k}Bx,", element, {displayMode:true,throwOnError:false});

where $var element = document.getElementById("moose-equation-f063629b-2f1b-497e-9c57-a7bfc8e0e8b7");katex.render("A", element, {displayMode:false,throwOnError:false});$ and $var element = document.getElementById("moose-equation-9ffe0e93-9bf6-4265-9397-4003cc759c70");katex.render("B", element, {displayMode:false,throwOnError:false});$ are linear or nonlinear operators represented by kernels. To differentiate the $var element = document.getElementById("moose-equation-78fefb74-4c3c-421a-ae12-1a13ecfef395");katex.render("B", element, {displayMode:false,throwOnError:false});$ kernels from the $var element = document.getElementById("moose-equation-b3931061-2323-41ce-9b93-11c771334dc5");katex.render("A", element, {displayMode:false,throwOnError:false});$ kernels, we must derive all $var element = document.getElementById("moose-equation-ff9ec76b-efc2-4909-92e0-4cd3a3aec90c");katex.render("B", element, {displayMode:false,throwOnError:false});$ kernels from EigenKernel. Currently we are only interested in the absolute minimum eigenvalue $var element = document.getElementById("moose-equation-cf05dd69-749a-4572-a854-dd605ecf233d");katex.render("\\frac{1}{k}", element, {displayMode:false,throwOnError:false});$ and the corresponding eigenvector $var element = document.getElementById("moose-equation-922bb4da-7cb6-42f8-ab28-efea8986f266");katex.render("x", element, {displayMode:false,throwOnError:false});$ of the system. We are also not seeking the solutions of a general nonlinear eigenvalue problem, where the operators have nonlinear dependency on the eigenvalue.

The inverse power method algorithm

Initialization
$var element = document.getElementById("moose-equation-042601a7-66c9-4043-b62c-328e3c8654c9");katex.render("\\begin{aligned} k^{(0)} &= k_0 \\\\ x^{(0)} &= x_0 \\end{aligned}", element, {displayMode:true,throwOnError:false});$
Update x and k
$var element = document.getElementById("moose-equation-f14b027a-9115-492b-9c3d-e3389a365800");katex.render("\\begin{aligned} x^{(n)} &= \\frac{1}{k^{(n-1)}} A^{-1}Bx^{(n-1)} \\\\ k^{(n)} &= k^{(n-1)} \\frac{|Bx^{(n)}|}{|Bx^{(n-1)}|} \\end{aligned}", element, {displayMode:true,throwOnError:false});$
Check the convergence
$var element = document.getElementById("moose-equation-4da940c1-fd9f-43af-ba0f-a19af3715005");katex.render("\\frac{|x^{(n)}-x^{(n-1)}|}{|x^{(n)}|} < tol_x", element, {displayMode:true,throwOnError:false});$
and
$var element = document.getElementById("moose-equation-a5a9532d-880f-48b6-a4b1-cc6827616e2b");katex.render("\\frac{|k^{(n)}-k^{(n-1)}|}{|k^{(n)}|} < tol_k", element, {displayMode:true,throwOnError:false});$
When either of them is not true, return Step 2, otherwise exit.

We notice immediately that $var element = document.getElementById("moose-equation-e20f833c-c25b-48b6-be41-3cece4d4951b");katex.render("\\frac{|Bx|}{k}", element, {displayMode:false,throwOnError:false});$ remains constant during the iteration, so if we make $var element = document.getElementById("moose-equation-6aaf65d6-799a-4d2b-85a5-e81ce3b107a4");katex.render("\\frac{|Bx^{(0)}|}{k^{(0)}}", element, {displayMode:false,throwOnError:false});$ equal to 1, the algorithm can be simplified a little:

Initialization
$var element = document.getElementById("moose-equation-412e621a-af6e-403e-96c3-37fe75ac3a8f");katex.render("\\begin{aligned} k^{(0)} &= k_0 \\\\ x^{(0)} &= k_0 \\frac{x_0}{|Bx_0|} \\end{aligned}", element, {displayMode:true,throwOnError:false});$
Update x and k
$var element = document.getElementById("moose-equation-4c9b2057-832f-4b79-ad85-f3330bd03bce");katex.render("\\begin{aligned} x^{(n)} &= \\frac{1}{k^{(n-1)}} A^{-1}Bx^{(n-1)} \\\\ k^{(n)} &= |Bx^{(n)}| \\end{aligned}", element, {displayMode:true,throwOnError:false});$
Check the convergence
$var element = document.getElementById("moose-equation-f21a1881-b5cf-4c99-b84e-1910bac773df");katex.render("\\frac{|x^{(n)}-x^{(n-1)}|}{|x^{(n)}|} < tol_x", element, {displayMode:true,throwOnError:false});$
and
$var element = document.getElementById("moose-equation-5a5c679e-6b73-4922-a94b-5baa098f3ddb");katex.render("\\frac{|k^{(n)}-k^{(n-1)}|}{|k^{(n)}|} < tol_k", element, {displayMode:true,throwOnError:false});$
When either of them is not true, return Step 2, otherwise exit.

Also in this simplified algorithm, the solution is automatically normalized making $var element = document.getElementById("moose-equation-e6e4b5d9-ce83-40d0-8380-266bb5365702");katex.render("|Bx|=k", element, {displayMode:false,throwOnError:false});$ . We can do postprocessing to normalize the solution so that $var element = document.getElementById("moose-equation-93e182e7-76a3-41fb-84bd-9917b932086e");katex.render("|x|=c", element, {displayMode:false,throwOnError:false});$ , where $var element = document.getElementById("moose-equation-5913e85d-d763-4ab8-bc5c-4ab56832114b");katex.render("|.|", element, {displayMode:false,throwOnError:false});$ can be any norm and $var element = document.getElementById("moose-equation-328352bb-766e-4253-9859-4de746cec136");katex.render("c", element, {displayMode:false,throwOnError:false});$ is a scalar constant.

If the minimum eigenvalue and the second smallest eigenvalue are close, i.e. the dominance ratio is about equal to one, the inverse power iteration converges very slowly. In such a case, we can apply accelerations, such as Chebyshev acceleration, based on the on-the-fly estimation of the dominance ratio.

The inverse power method is appealing because we can apply matrix-free schemes on evaluating $var element = document.getElementById("moose-equation-7212fa1b-20d6-4797-858e-4bca351be914");katex.render("Ax - \\frac{1}{k}Bx", element, {displayMode:false,throwOnError:false});$ . We can use PJFNK for inverting $var element = document.getElementById("moose-equation-e6d8d7cc-7442-4aa9-a899-00a5b60908f7");katex.render("A", element, {displayMode:false,throwOnError:false});$ and we do not have to exactly assemble matrix $var element = document.getElementById("moose-equation-12eecc9b-946c-41a1-b8ef-913253e03475");katex.render("A", element, {displayMode:false,throwOnError:false});$ for the preconditioning purpose.

Input Parameters

bx_normTo evaluate |Bx| for the eigenvalue
C++ Type:PostprocessorName
Unit:(no unit assumed)
Controllable:No
Description:To evaluate |Bx| for the eigenvalue

Required Parameters

Chebyshev_acceleration_onTrueIf Chebyshev acceleration is turned on
Default:True
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:If Chebyshev acceleration is turned on
eig_check_tol1e-06Eigenvalue convergence tolerance
Default:1e-06
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Eigenvalue convergence tolerance
k01Initial guess of the eigenvalue
Default:1
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Initial guess of the eigenvalue
max_power_iterations300The maximum number of power iterations
Default:300
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:The maximum number of power iterations
min_power_iterations1Minimum number of power iterations
Default:1
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:Minimum number of power iterations
sol_check_tol1.79769e+308Convergence tolerance on |x-x_previous| when provided
Default:1.79769e+308
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Convergence tolerance on |x-x_previous| when provided
verboseFalseSet to true to print additional information
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Set to true to print additional information
xdiffTo evaluate |x-x_previous| for power iterations
C++ Type:PostprocessorName
Unit:(no unit assumed)
Controllable:No
Description:To evaluate |x-x_previous| for power iterations

Optional Parameters

accept_on_max_fixed_point_iterationFalseTrue to treat reaching the maximum number of fixed point iterations as converged.
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:True to treat reaching the maximum number of fixed point iterations as converged.
auto_advanceFalseWhether to automatically advance sub-applications regardless of whether their solve converges, for transient executioners only.
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Whether to automatically advance sub-applications regardless of whether their solve converges, for transient executioners only.
custom_abs_tol1e-50The absolute nonlinear residual to shoot for during fixed point iterations. This check is performed based on postprocessor defined by the custom_pp residual.
Default:1e-50
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:The absolute nonlinear residual to shoot for during fixed point iterations. This check is performed based on postprocessor defined by the custom_pp residual.
custom_ppPostprocessor for custom fixed point convergence check.
C++ Type:PostprocessorName
Unit:(no unit assumed)
Controllable:No
Description:Postprocessor for custom fixed point convergence check.
custom_rel_tol1e-08The relative nonlinear residual drop to shoot for during fixed point iterations. This check is performed based on the postprocessor defined by custom_pp residual.
Default:1e-08
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:The relative nonlinear residual drop to shoot for during fixed point iterations. This check is performed based on the postprocessor defined by custom_pp residual.
direct_pp_valueFalseTrue to use direct postprocessor value (scaled by value on first iteration). False (default) to use difference in postprocessor value between fixed point iterations.
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:True to use direct postprocessor value (scaled by value on first iteration). False (default) to use difference in postprocessor value between fixed point iterations.
disable_fixed_point_residual_norm_checkFalseDisable the residual norm evaluation thus the three parameters fixed_point_rel_tol, fixed_point_abs_tol and fixed_point_force_norms.
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Disable the residual norm evaluation thus the three parameters fixed_point_rel_tol, fixed_point_abs_tol and fixed_point_force_norms.
fixed_point_abs_tol1e-50The absolute nonlinear residual to shoot for during fixed point iterations. This check is performed based on the main app's nonlinear residual.
Default:1e-50
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:The absolute nonlinear residual to shoot for during fixed point iterations. This check is performed based on the main app's nonlinear residual.
fixed_point_algorithmpicardThe fixed point algorithm to converge the sequence of problems.
Default:picard
C++ Type:MooseEnum
Unit:(no unit assumed)
Options:picard, secant, steffensen
Controllable:No
Description:The fixed point algorithm to converge the sequence of problems.
fixed_point_force_normsFalseForce the evaluation of both the TIMESTEP_BEGIN and TIMESTEP_END norms regardless of the existence of active MultiApps with those execute_on flags, default: false.
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Force the evaluation of both the TIMESTEP_BEGIN and TIMESTEP_END norms regardless of the existence of active MultiApps with those execute_on flags, default: false.
fixed_point_max_its1Specifies the maximum number of fixed point iterations.
Default:1
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:Specifies the maximum number of fixed point iterations.
fixed_point_min_its1Specifies the minimum number of fixed point iterations.
Default:1
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:Specifies the minimum number of fixed point iterations.
fixed_point_rel_tol1e-08The relative nonlinear residual drop to shoot for during fixed point iterations. This check is performed based on the main app's nonlinear residual.
Default:1e-08
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:The relative nonlinear residual drop to shoot for during fixed point iterations. This check is performed based on the main app's nonlinear residual.
relaxation_factor1Fraction of newly computed value to keep.Set between 0 and 2.
Default:1
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Fraction of newly computed value to keep.Set between 0 and 2.
transformed_postprocessorsList of main app postprocessors to transform during fixed point iterations
C++ Type:std::vector<PostprocessorName>
Unit:(no unit assumed)
Controllable:No
Description:List of main app postprocessors to transform during fixed point iterations
transformed_variablesList of main app variables to transform during fixed point iterations
C++ Type:std::vector<std::string>
Unit:(no unit assumed)
Controllable:No
Description:List of main app variables to transform during fixed point iterations

Fixed Point Iterations Parameters

auto_initializationTrueTrue to ask the solver to set initial
Default:True
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:True to ask the solver to set initial
control_tagsAdds user-defined labels for accessing object parameters via control logic.
C++ Type:std::vector<std::string>
Unit:(no unit assumed)
Controllable:No
Description:Adds user-defined labels for accessing object parameters via control logic.
enableTrueSet the enabled status of the MooseObject.
Default:True
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Set the enabled status of the MooseObject.
outputsVector of output names where you would like to restrict the output of variables(s) associated with this object
C++ Type:std::vector<OutputName>
Unit:(no unit assumed)
Controllable:No
Description:Vector of output names where you would like to restrict the output of variables(s) associated with this object
skip_exception_checkFalseSpecifies whether or not to skip exception check
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Specifies whether or not to skip exception check
time0System time
Default:0
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:System time

Advanced Parameters

automatic_scalingFalseWhether to use automatic scaling for the variables.
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Whether to use automatic scaling for the variables.
compute_scaling_once1 Whether the scaling factors should only be computed once at the beginning of the simulation through an extra Jacobian evaluation. If this is set to false, then the scaling factors will be computed during an extra Jacobian evaluation at the beginning of every time step. Vector entries correspond to each nonlinear system.
Default:1
C++ Type:std::vector<bool>
Unit:(no unit assumed)
Controllable:No
Description:Whether the scaling factors should only be computed once at the beginning of the simulation through an extra Jacobian evaluation. If this is set to false, then the scaling factors will be computed during an extra Jacobian evaluation at the beginning of every time step. Vector entries correspond to each nonlinear system.
ignore_variables_for_autoscalingList of variables that do not participate in autoscaling. Vector entries correspond to each nonlinear system.
C++ Type:std::vector<std::vector<std::string>>
Unit:(no unit assumed)
Controllable:No
Description:List of variables that do not participate in autoscaling. Vector entries correspond to each nonlinear system.
off_diagonals_in_auto_scaling0 Whether to consider off-diagonals when determining automatic scaling factors. Vector entries correspond to each nonlinear system.
Default:0
C++ Type:std::vector<bool>
Unit:(no unit assumed)
Controllable:No
Description:Whether to consider off-diagonals when determining automatic scaling factors. Vector entries correspond to each nonlinear system.
resid_vs_jac_scaling_param0 A parameter that indicates the weighting of the residual vs the Jacobian in determining variable scaling parameters. A value of 1 indicates pure residual-based scaling. A value of 0 indicates pure Jacobian-based scaling. Vector entries correspond to each nonlinear system.
Default:0
C++ Type:std::vector<double>
Unit:(no unit assumed)
Controllable:No
Description:A parameter that indicates the weighting of the residual vs the Jacobian in determining variable scaling parameters. A value of 1 indicates pure residual-based scaling. A value of 0 indicates pure Jacobian-based scaling. Vector entries correspond to each nonlinear system.
scaling_group_variablesName of variables that are grouped together for determining scale factors. (Multiple groups can be provided, separated by semicolon). Vector entries correspond to each nonlinear system.
C++ Type:std::vector<std::vector<std::vector<std::string, std::allocator<std::string>>>>
Unit:(no unit assumed)
Controllable:No
Description:Name of variables that are grouped together for determining scale factors. (Multiple groups can be provided, separated by semicolon). Vector entries correspond to each nonlinear system.

Solver Variable Scaling Parameters

contact_line_search_allowed_lambda_cuts2The number of times lambda is allowed to be cut in half in the contact line search. We recommend this number be roughly bounded by 0 <= allowed_lambda_cuts <= 3
Default:2
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:The number of times lambda is allowed to be cut in half in the contact line search. We recommend this number be roughly bounded by 0 <= allowed_lambda_cuts <= 3
contact_line_search_ltolThe linear relative tolerance to be used while the contact state is changing between non-linear iterations. We recommend that this tolerance be looser than the standard linear tolerance
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:The linear relative tolerance to be used while the contact state is changing between non-linear iterations. We recommend that this tolerance be looser than the standard linear tolerance
line_searchdefaultSpecifies the line search type (Note: none = basic)
Default:default
C++ Type:MooseEnum
Unit:(no unit assumed)
Options:basic, bt, contact, cp, default, l2, none, project, shell
Controllable:No
Description:Specifies the line search type (Note: none = basic)
line_search_packagepetscThe solver package to use to conduct the line-search
Default:petsc
C++ Type:MooseEnum
Unit:(no unit assumed)
Options:petsc, moose
Controllable:No
Description:The solver package to use to conduct the line-search

Solver Line Search Parameters

l_abs_tol1e-50Linear Absolute Tolerance
Default:1e-50
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Linear Absolute Tolerance
l_max_its10000Max Linear Iterations
Default:10000
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:Max Linear Iterations
l_tol0.01Linear Relative Tolerance
Default:0.01
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Linear Relative Tolerance
reuse_preconditionerFalseIf true reuse the previously calculated preconditioner for the linearized system across multiple solves spanning nonlinear iterations and time steps. The preconditioner resets as controlled by reuse_preconditioner_max_linear_its
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:If true reuse the previously calculated preconditioner for the linearized system across multiple solves spanning nonlinear iterations and time steps. The preconditioner resets as controlled by reuse_preconditioner_max_linear_its
reuse_preconditioner_max_linear_its25Reuse the previously calculated preconditioner for the linear system until the number of linear iterations exceeds this number
Default:25
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:Reuse the previously calculated preconditioner for the linear system until the number of linear iterations exceeds this number

Linear Solver Parameters

max_xfem_update4294967295Maximum number of times to update XFEM crack topology in a step due to evolving cracks
Default:4294967295
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:Maximum number of times to update XFEM crack topology in a step due to evolving cracks
update_xfem_at_timestep_beginFalseShould XFEM update the mesh at the beginning of the timestep
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Should XFEM update the mesh at the beginning of the timestep

Xfem Fixed Point Iterations Parameters

mffd_typewpSpecifies the finite differencing type for Jacobian-free solve types. Note that the default is wp (for Walker and Pernice).
Default:wp
C++ Type:MooseEnum
Unit:(no unit assumed)
Options:wp, ds
Controllable:No
Description:Specifies the finite differencing type for Jacobian-free solve types. Note that the default is wp (for Walker and Pernice).
petsc_optionsSingleton PETSc options
C++ Type:MultiMooseEnum
Unit:(no unit assumed)
Options:-dm_moose_print_embedding, -dm_view, -ksp_converged_reason, -ksp_gmres_modifiedgramschmidt, -ksp_monitor, -ksp_monitor_snes_lg-snes_ksp_ew, -ksp_snes_ew, -snes_converged_reason, -snes_ksp, -snes_ksp_ew, -snes_linesearch_monitor, -snes_mf, -snes_mf_operator, -snes_monitor, -snes_test_display, -snes_view
Controllable:No
Description:Singleton PETSc options
petsc_options_inameNames of PETSc name/value pairs
C++ Type:MultiMooseEnum
Unit:(no unit assumed)
Options:-ksp_atol, -ksp_gmres_restart, -ksp_max_it, -ksp_pc_side, -ksp_rtol, -ksp_type, -mat_fd_coloring_err, -mat_fd_type, -mat_mffd_type, -pc_asm_overlap, -pc_factor_levels, -pc_factor_mat_ordering_type, -pc_hypre_boomeramg_grid_sweeps_all, -pc_hypre_boomeramg_max_iter, -pc_hypre_boomeramg_strong_threshold, -pc_hypre_type, -pc_type, -snes_atol, -snes_linesearch_type, -snes_ls, -snes_max_it, -snes_rtol, -snes_divergence_tolerance, -snes_type, -sub_ksp_type, -sub_pc_type
Controllable:No
Description:Names of PETSc name/value pairs
petsc_options_valueValues of PETSc name/value pairs (must correspond with "petsc_options_iname"
C++ Type:std::vector<std::string>
Unit:(no unit assumed)
Controllable:No
Description:Values of PETSc name/value pairs (must correspond with "petsc_options_iname"

Petsc Parameters

multi_system_fixed_pointFalseWhether to perform fixed point (Picard) iterations between the nonlinear systems.
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Whether to perform fixed point (Picard) iterations between the nonlinear systems.
multi_system_fixed_point_convergenceConvergence object to determine the convergence of the multi-system fixed point iteration. If unspecified, defaults to checking that every system is converged (based on their own convergence criterion)
C++ Type:ConvergenceName
Unit:(no unit assumed)
Controllable:No
Description:Convergence object to determine the convergence of the multi-system fixed point iteration. If unspecified, defaults to checking that every system is converged (based on their own convergence criterion)
system_namesNames of the solver systems (both linear and nonlinear) that will be solved
C++ Type:std::vector<SolverSystemName>
Unit:(no unit assumed)
Controllable:No
Description:Names of the solver systems (both linear and nonlinear) that will be solved

Multiple Solver System Parameters

n_max_nonlinear_pingpong100The maximum number of times the nonlinear residual can ping pong before requesting halting the current evaluation and requesting timestep cut for transient simulations
Default:100
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:The maximum number of times the nonlinear residual can ping pong before requesting halting the current evaluation and requesting timestep cut for transient simulations
nl_abs_div_tol1e+50Nonlinear Absolute Divergence Tolerance. A negative value disables this check.
Default:1e+50
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Nonlinear Absolute Divergence Tolerance. A negative value disables this check.
nl_abs_step_tol0Nonlinear Absolute step Tolerance
Default:0
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Nonlinear Absolute step Tolerance
nl_abs_tol1e-50Nonlinear Absolute Tolerance
Default:1e-50
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Nonlinear Absolute Tolerance
nl_div_tol1e+10Nonlinear Relative Divergence Tolerance. A negative value disables this check.
Default:1e+10
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Nonlinear Relative Divergence Tolerance. A negative value disables this check.
nl_forced_its0The Number of Forced Nonlinear Iterations
Default:0
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:The Number of Forced Nonlinear Iterations
nl_max_funcs10000Max Nonlinear solver function evaluations
Default:10000
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:Max Nonlinear solver function evaluations
nl_max_its50Max Nonlinear Iterations
Default:50
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:Max Nonlinear Iterations
nl_rel_step_tol0Nonlinear Relative step Tolerance
Default:0
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Nonlinear Relative step Tolerance
nl_rel_tol1e-08Nonlinear Relative Tolerance
Default:1e-08
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Nonlinear Relative Tolerance
nonlinear_convergenceName of the Convergence object(s) to use to assess convergence of the nonlinear system(s) solve. If not provided, the default Convergence associated with the Problem will be constructed internally.
C++ Type:std::vector<ConvergenceName>
Unit:(no unit assumed)
Controllable:No
Description:Name of the Convergence object(s) to use to assess convergence of the nonlinear system(s) solve. If not provided, the default Convergence associated with the Problem will be constructed internally.
num_grids1The number of grids to use for a grid sequencing algorithm. This includes the final grid, so num_grids = 1 indicates just one solve in a time-step
Default:1
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:The number of grids to use for a grid sequencing algorithm. This includes the final grid, so num_grids = 1 indicates just one solve in a time-step
residual_and_jacobian_together0 Whether to compute the residual and Jacobian together. Vector entries correspond to each nonlinear system.
Default:0
C++ Type:std::vector<bool>
Unit:(no unit assumed)
Controllable:No
Description:Whether to compute the residual and Jacobian together. Vector entries correspond to each nonlinear system.
snesmf_reuse_baseTrueSpecifies whether or not to reuse the base vector for matrix-free calculation
Default:True
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Specifies whether or not to reuse the base vector for matrix-free calculation
solve_typePJFNK: Preconditioned Jacobian-Free Newton Krylov JFNK: Jacobian-Free Newton Krylov NEWTON: Full Newton Solve FD: Use finite differences to compute Jacobian LINEAR: Solving a linear problem
C++ Type:MooseEnum
Unit:(no unit assumed)
Options:PJFNK, JFNK, NEWTON, FD, LINEAR
Controllable:No
Description:PJFNK: Preconditioned Jacobian-Free Newton Krylov JFNK: Jacobian-Free Newton Krylov NEWTON: Full Newton Solve FD: Use finite differences to compute Jacobian LINEAR: Solving a linear problem
splittingTop-level splitting defining a hierarchical decomposition into subsystems to help the solver. Outer-vector of this vector-of-vector parameter correspond to each nonlinear system.
C++ Type:std::vector<std::vector<std::string>>
Unit:(no unit assumed)
Controllable:No
Description:Top-level splitting defining a hierarchical decomposition into subsystems to help the solver. Outer-vector of this vector-of-vector parameter correspond to each nonlinear system.
use_pre_SMO_residualFalseCompute the pre-SMO residual norm and use it in the relative convergence check. The pre-SMO residual is computed at the begining of the time step before solution-modifying objects are executed. Solution-modifying objects include preset BCs, constraints, predictors, etc.
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Compute the pre-SMO residual norm and use it in the relative convergence check. The pre-SMO residual is computed at the begining of the time step before solution-modifying objects are executed. Solution-modifying objects include preset BCs, constraints, predictors, etc.

Nonlinear Solver Parameters

normal_factorNormalize x to make |x| equal to this factor
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Normalize x to make |x| equal to this factor
normalizationTo evaluate |x| for normalization
C++ Type:PostprocessorName
Unit:(no unit assumed)
Controllable:No
Description:To evaluate |x| for normalization
output_before_normalizationTrueTrue to output a step before normalization
Default:True
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:True to output a step before normalization

Normalization Parameters

Restart Parameters

Overview
The inverse power method algorithm
Input Parameters