gemseo / algos / linear_solvers

lib_scipy_linalg module¶

Wrappers for scipy’s linear solvers.

Classes:

ScipyLinalgAlgos()

Wrapper for scipy linalg sparse linear solvers.

class gemseo.algos.linear_solvers.lib_scipy_linalg.ScipyLinalgAlgos[source]¶

Bases: gemseo.algos.linear_solvers.linear_solver_lib.LinearSolverLib

Wrapper for scipy linalg sparse linear solvers.

Return type: None

save_fpath¶

The path to the file to saved the problem when it is not converged and the option save_when_fail is active.

Type: str

methods_map¶

The mapping between the solver names and the solvers methods in scipy.sparse.

Type: Dict[str, Callable]

Attributes:

`BASE_INFO_MSG`
`INTERNAL_NAME`
`LGMRES_SPEC_OPTS`
`LHS_CAN_BE_LINEAR_OPERATOR`
`LHS_MUST_BE_POSITIVE_DEFINITE`
`LHS_MUST_BE_SYMMETRIC`
`LIB`
`OPTIONS_DIR`
`OPTIONS_MAP`
`PROBLEM_TYPE`
`SAVE_WHEN_FAIL`
`algorithms`	The available algorithms.
`solution`	The solution of the problem.

Methods:

`driver_has_option`(option_key)	Check if the option key exists.
`execute`(problem[, algo_name])	Executes the driver.
`filter_adapted_algorithms`(problem)	Filter the algorithms capable of solving the problem.
`get_default_properties`(algo_name)	Return the properties of the algorithm.
`init_options_grammar`(algo_name)	Initialize the options grammar.
`is_algorithm_suited`(algo_dict, problem)	Check if the algorithm is suited to the problem according to algo_dict.
`solve`(linear_problem, algo_name, **options)	Solve the linear system.

BASE_INFO_MSG = 'scipy linear solver algorithm stop info: '¶

INTERNAL_NAME = 'internal_algo_name'¶

LGMRES_SPEC_OPTS = ('inner_m', 'outer_k', 'outer_v', 'store_outer_av', 'prepend_outer_v')¶

LHS_CAN_BE_LINEAR_OPERATOR = 'LHS_linear_operator'¶

LHS_MUST_BE_POSITIVE_DEFINITE = 'LHS_positive_definite'¶

LHS_MUST_BE_SYMMETRIC = 'LHS_symmetric'¶

LIB = 'lib'¶

OPTIONS_DIR = 'options'¶

OPTIONS_MAP = {'max_iter': 'maxiter', 'preconditioner': 'M', 'store_outer_av': 'store_outer_Av'}¶

PROBLEM_TYPE = 'problem_type'¶

SAVE_WHEN_FAIL = 'save_when_fail'¶

property algorithms¶: The available algorithms.

driver_has_option(option_key)¶

Check if the option key exists.

Parameters: option_key (str) – The name of the option.
Returns: Whether the option is in the grammar.
Return type: bool

execute(problem, algo_name=None, **options)¶

Executes the driver.

Parameters

problem (Any) – The problem to be solved.
algo_name (Optional[str]) –
The name of the algorithm> If None, use the algo_name attribute which may have been set by the factory.

By default it is set to None.
options – The options dict for the algorithm.
**options (Any) –

Return type

None

filter_adapted_algorithms(problem)¶

Filter the algorithms capable of solving the problem.

Parameters: problem (Any) – The opt_problem to be solved.
Returns: The list of adapted algorithms names.
Return type: bool

classmethod get_default_properties(algo_name)[source]¶

Return the properties of the algorithm.

It states if it requires symmetric, or positive definite matrices for instance.

Parameters: algo_name (str) – The algorithm name.
Returns: The properties of the solver.
Return type: Dict[str, Union[bool, str]]

init_options_grammar(algo_name)¶

Initialize the options grammar.

Parameters: algo_name (str) – The name of the algorithm.
Return type: gemseo.core.grammars.json_grammar.JSONGrammar

static is_algorithm_suited(algo_dict, problem)¶

Check if the algorithm is suited to the problem according to algo_dict.

Parameters

algo_dict (Mapping[str, bool]) – The algorithm characteristics.
problem (gemseo.algos.linear_solvers.linear_problem.LinearProblem) – The problem to be solved.

Returns

Whether the algorithm suits.

Return type

bool

property solution¶: The solution of the problem.

solve(linear_problem, algo_name, **options)¶

Solve the linear system.

Parameters

linear_problem (gemseo.algos.linear_solvers.linear_problem.LinearProblem) – The problem to solve.
algo_name (str) – The name of the algorithm.
**options (Any) – The algorithm options.

Returns

The execution result.

Return type

Any