"""Define :class:`LeastSquares`, a simple and fast optimization method."""
from scipy.optimize import least_squares
from lightwin.optimisation.algorithms.algorithm import OptiSol
from lightwin.optimisation.algorithms.downhill_simplex import DownhillSimplex
[docs]
class LeastSquares(DownhillSimplex):
"""Plain least-squares method, efficient for small problems.
See also
--------
:class:`.LeastSquaresPenalty`
"""
supports_constraints = False
[docs]
def optimize(self) -> OptiSol:
"""Set up the optimization and solve the problem.
Returns
-------
Gives list of solutions, corresponding objective, convergence
violation if applicable, etc.
"""
x_0, bounds = self._format_variables()
result = least_squares(
fun=self._wrapper_residuals,
x0=x_0,
bounds=bounds,
**self.optimisation_algorithm_kwargs,
)
self.opti_sol = self._generate_opti_sol(result)
complementary_info = ("Least-Squares algorithm", result.message)
self._finalize(self.opti_sol, *complementary_info)
return self.opti_sol
@property
def _default_kwargs(self) -> dict:
"""Create the ``kwargs`` for the optimisation."""
kwargs = {
"jac": "2-point", # Default
# 'trf' not ideal as jac is not sparse. 'dogbox' may have
# difficulties with rank-defficient jacobian.
"method": "dogbox",
"ftol": 1e-10,
"gtol": 1e-8,
"xtol": 1e-8,
# 'x_scale': 'jac',
# 'loss': 'arctan',
"diff_step": None,
"tr_solver": None,
"tr_options": {},
"jac_sparsity": None,
"verbose": 0,
}
return kwargs