Conjugate multiple problems.
Allows to define conjugate multiple problems.
Kind: ‘ls.cm_pb’
For common configuration parameters, see Solver.
Specific configuration parameters:
Parameters: | method : {‘auto’, ‘umfpack’, ‘superlu’} (default: ‘auto’)
presolve : bool (default: False)
warn : bool (default: True)
others : list
coupling_variables : list
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PETSc Krylov subspace solver.
The solver and preconditioner types are set upon the solver object creation. Tolerances can be overriden when called by passing a conf object.
Convergence is reached when rnorm < max(eps_r * rnorm_0, eps_a), where, in PETSc, rnorm is by default the norm of preconditioned residual.
Kind: ‘ls.petsc’
For common configuration parameters, see Solver.
Specific configuration parameters:
Parameters: | method : str (default: ‘cg’)
precond : str (default: ‘icc’)
precond_side : {‘left’, ‘right’, ‘symmetric’, None}
i_max : int (default: 100)
eps_a : float (default: 1e-08)
eps_r : float (default: 1e-08)
eps_d : float (default: 100000.0)
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PETSc Krylov subspace solver able to run in parallel by storing the system to disk and running a separate script via mpiexec.
The solver and preconditioner types are set upon the solver object creation. Tolerances can be overriden when called by passing a conf object.
Convergence is reached when rnorm < max(eps_r * rnorm_0, eps_a), where, in PETSc, rnorm is by default the norm of preconditioned residual.
Kind: ‘ls.petsc_parallel’
For common configuration parameters, see Solver.
Specific configuration parameters:
Parameters: | method : str (default: ‘cg’)
precond : str (default: ‘icc’)
precond_side : {‘left’, ‘right’, ‘symmetric’, None}
i_max : int (default: 100)
eps_a : float (default: 1e-08)
eps_r : float (default: 1e-08)
eps_d : float (default: 100000.0)
log_dir : str (default: ‘.’)
n_proc : int (default: 1)
sub_precond : str (default: ‘icc’)
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Interface to PyAMG solvers.
Kind: ‘ls.pyamg’
For common configuration parameters, see Solver.
Specific configuration parameters:
Parameters: | method : str (default: ‘smoothed_aggregation_solver’)
accel : str
i_max : int (default: 100)
eps_r : float (default: 1e-08)
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Schur complement.
Solution of the linear system
\left[ \begin{array}{cc} A & B \\ C & D \end{array} \right] \cdot \left[ \begin{array}{c} u \\ v \end{array} \right] = \left[ \begin{array}{c} f \\ g \end{array} \right]
is obtained by solving the following equation:
(D - C A^{-1} B) \cdot v = g - C A^{-1} f
variable(s) u are specified in “eliminate” list, variable(s) v are specified in “keep” list,
See: http://en.wikipedia.org/wiki/Schur_complement
Kind: ‘ls.schur_complement’
For common configuration parameters, see Solver.
Specific configuration parameters:
Parameters: | method : {‘auto’, ‘umfpack’, ‘superlu’} (default: ‘auto’)
presolve : bool (default: False)
warn : bool (default: True)
eliminate : list
keep : list
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Generalized Schur complement.
Defines the matrix blocks and calls user defined function.
Kind: ‘ls.schur_generalized’
For common configuration parameters, see Solver.
Specific configuration parameters:
Parameters: | method : {‘auto’, ‘umfpack’, ‘superlu’} (default: ‘auto’)
presolve : bool (default: False)
warn : bool (default: True)
blocks : dict
function : callable
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Direct sparse solver from SciPy.
Kind: ‘ls.scipy_direct’
For common configuration parameters, see Solver.
Specific configuration parameters:
Parameters: | method : {‘auto’, ‘umfpack’, ‘superlu’} (default: ‘auto’)
presolve : bool (default: False)
warn : bool (default: True)
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Interface to SciPy iterative solvers.
The eps_r tolerance is both absolute and relative - the solvers stop when either the relative or the absolute residual is below it.
A preconditioner can be anything that the SciPy solvers accept (sparse matrix, dense matrix, LinearOperator).
Kind: ‘ls.scipy_iterative’
For common configuration parameters, see Solver.
Specific configuration parameters:
Parameters: | method : str (default: ‘cg’)
precond : {sparse matrix, dense matrix, LinearOperator}
callback : function
i_max : int (default: 100)
eps_r : float (default: 1e-08)
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