"""Optiland Scipy Optimization Module
This module contains classes for various optimization algorithms that can be
used to solve optimization problems defined in the OptimizationProblem class.
This module provides a generic optimizer class and several specific optimizers
that utilize different algorithms from the SciPy library.
Kramer Harrison, 2024
"""
from __future__ import annotations
import warnings
from typing import TYPE_CHECKING
import optiland.backend as be
from scipy import optimize
from ..base import BaseOptimizer
if TYPE_CHECKING:
from ...problem import OptimizationProblem
[docs]
class OptimizerGeneric(BaseOptimizer):
"""Generic optimizer class for solving optimization problems.
Args:
problem (OptimizationProblem): The optimization problem to be solved.
Attributes:
problem (OptimizationProblem): The optimization problem to be solved.
_x (list): List to store the values of the variables during
optimization.
Methods:
optimize(maxiter=1000, disp=True, tol=1e-3): Optimize the problem
using the specified parameters.
undo(): Undo the last optimization step.
_fun(x): Internal function to evaluate the objective function.
"""
def __init__(self, problem: OptimizationProblem):
super().__init__(problem)
self._x = []
assert not any(isinstance(var.value, str) for var in self.problem.variables), (
"Glass material(s) have been declared as variable(s). "
"Please use GlassExpert or remove them."
)
if self.problem.initial_value == 0.0:
self.problem.initial_value = self.problem.sum_squared()
[docs]
def optimize(self, method=None, maxiter=1000, disp=True, tol=1e-3, callback=None):
"""Optimize the problem using the specified parameters.
Args:
method (str, optional): The optimization method to use. Default is
chosen to be one of BFGS, L-BFGS-B, SLSQP, depending on
whether contraints or bounds given. Follows
scipy.optimize.minimize method.
maxiter (int, optional): Maximum number of iterations.
Default is 1000.
disp (bool, optional): Whether to display optimization information.
Default is True.
tol (float, optional): Tolerance for convergence. Default is 1e-3.
callback (callable): A callable called after each iteration.
Returns:
result (OptimizeResult): The optimization result.
"""
if method == "Default":
method = None
x0 = [var.value for var in self.problem.variables]
self._x.append(x0)
x0 = be.to_numpy(x0)
bounds = tuple([var.bounds for var in self.problem.variables])
options = {"maxiter": maxiter, "disp": disp}
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
result = optimize.minimize(
self._fun,
x0,
method=method,
bounds=bounds,
options=options,
tol=tol,
callback=callback,
)
# The last function evaluation is not necessarily the lowest.
# Update all lens variables to their optimized values
for idvar, var in enumerate(self.problem.variables):
var.update(result.x[idvar])
self.problem.update_optics()
return result
[docs]
def undo(self):
"""Undo the last optimization step."""
if len(self._x) > 0:
x0 = self._x[-1]
for idvar, var in enumerate(self.problem.variables):
var.update(x0[idvar])
self._x.pop(-1)
[docs]
def _fun(self, x) -> float:
"""Internal function to evaluate the objective function.
Args:
x (array-like): The values of the variables.
Returns:
rss (float): The residual sum of squares.
"""
# Update all variables to their new values
for idvar, var in enumerate(self.problem.variables):
var.update(be.array(x[idvar]))
# Update optics (e.g., pickups and solves)
self.problem.update_optics()
# Compute merit function value
try:
rss = self.problem.sum_squared()
if be.isnan(rss):
return 1e10
# --- Convert result back to float for SciPy ---
return be.to_numpy(rss).item()
except ValueError:
return 1e10