from __future__ import annotations
import warnings
from typing import TYPE_CHECKING
import optiland.backend as be
from scipy import optimize
from .base import OptimizerGeneric
if TYPE_CHECKING:
from ...problem import OptimizationProblem
[docs]
class BasinHopping(OptimizerGeneric):
"""Basin-hopping optimizer for solving optimization problems.
Args:
problem (OptimizationProblem): The optimization problem to be solved.
Methods:
optimize(maxiter=1000, disp=True, workers=-1): Runs the basin-hopping
optimization algorithm.
"""
def __init__(self, problem: OptimizationProblem):
"""Initializes a new instance of the BasinHopping class.
Args:
problem (OptimizationProblem): The optimization problem to be
solved.
"""
super().__init__(problem)
[docs]
def optimize(self, niter=100, callback=None, *args, **kwargs):
"""Runs the basin-hopping algorithm. Note that the basin-hopping
algorithm accepts the same arguments as the
scipy.optimize.basinhopping function.
Args:
niter (int): Number of iterations to perform. Default is 100.
callback (callable): A callable called after each iteration.
*args: Variable length argument list.
**kwargs: Arbitrary keyword arguments.
Returns:
result (OptimizeResult): The optimization result.
Raises:
ValueError: If any variable in the problem does not have bounds.
"""
# Get initial values in backend format
x0_backend = [var.value for var in self.problem.variables]
self._x.append(x0_backend) # Store backend values
# Convert x0 to NumPy for SciPy
x0_numpy = be.to_numpy(x0_backend)
bounds = tuple([var.bounds for var in self.problem.variables])
if not all(x is None for pair in bounds for x in pair):
raise ValueError("Basin-hopping does not accept bounds.")
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
result = optimize.basinhopping(
self._fun,
x0=x0_numpy,
niter=niter,
callback=callback,
**kwargs,
)
for idvar, var in enumerate(self.problem.variables):
var.update(result.x[idvar])
self.problem.update_optics()
return result