smt_optim.utils package#

Submodules#

smt_optim.utils.constraints module#

smt_optim.utils.constraints.compute_rscv(c: ndarray, c_type: list[str] | None, g_tol: float = 0.0, h_tol: float = 0.0) → ndarray[source]#
smt_optim.utils.constraints.compute_rscv_sp(x: ndarray, cstr_list: list[dict]) → float[source]#

SciPy wrapper to compute the Root Squared Constraint Violation (RSCV).

Parameters:

  • x

  • cstr_list

Returns:

smt_optim.utils.get_fmin module#

smt_optim.utils.get_fmin.get_fmin(f: ndarray, c: ndarray | None = None, c_type: list[str] | None = None, rscv_tol: float = 0.0) → float[source]#

smt_optim.utils.initial_design module#

smt_optim.utils.initial_design.generate_initial_design(state: State, evaluator, config) → None[source]#

smt_optim.utils.json module#

smt_optim.utils.json.json_safe(obj)[source]#

smt_optim.utils.logger module#

class smt_optim.utils.logger.ConsoleLogger(config)[source]#

Bases: object

on_iter_end(state) → None[source]#
print_header()[source]#
class smt_optim.utils.logger.JsonLogger(config)[source]#

Bases: object

on_iter_end(state) → None[source]#
smt_optim.utils.logger.format_value(v, fmt)[source]#

smt_optim.utils.stop_criteria module#

smt_optim.utils.stop_criteria.check_stop_criteria(state, config) → bool[source]#
smt_optim.utils.stop_criteria.compute_budget(state) → float[source]#