# Copyright 2025 Qilimanjaro Quantum Tech
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Callable
from scipy import optimize as scipy_optimize
from qilisdk.yaml import yaml
from .optimizer import Optimizer
from .optimizer_result import OptimizerIntermediateResult, OptimizerResult
if TYPE_CHECKING:
from scipy.optimize import OptimizeResult
@yaml.register_class
[documents]
class SciPyOptimizer(Optimizer):
def __init__(
self,
method: str | Callable | None = None,
**kwargs: dict[str, Any],
) -> None:
"""Create a new Gradient Based optimizer instance.
Args:
method (str | Callable | None, optional):Type of solver. Should be one of
- 'Nelder-Mead
- 'Powell'
- 'CG'
- 'BFGS'
- 'Newton-CG'
- 'L-BFGS-B'
- 'TNC'
- 'COBYLA'
- 'COBYQA'
- 'SLSQP'
- 'trust-constr
- 'dogleg'
- 'trust-ncg'
- 'trust-exact'
- 'trust-krylov'
- 'basinhopping' (global)
- 'direct' (global)
- 'dual_annealing' (global)
- 'differential_evolution' (global)
- 'shgo' (global)
- custom - a callable object, see `scipy.optimize.minimize <https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html>`__ for description.
If not given, chosen to be one of ``BFGS``, ``L-BFGS-B``, ``SLSQP``,
depending on whether or not the problem has constraints or bounds.
bounds (list[tuple[int, int]] | None, optional):
Bounds on variables for Nelder-Mead, L-BFGS-B, TNC, SLSQP, Powell,
trust-constr, COBYLA, and COBYQA methods. To specify it you can provide a sequence of ``(min, max)`` pairs
for each element in parameter list.
Extra Args:
Any argument supported by `scipy.optimize.minimize <https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html>` can be passed.
Note: the parameters, cost function and the ``args`` that are passed to this function will be specified in the optimize method. Moreover, callbacks are not supported for the moment.
"""
super().__init__()
[documents]
def optimize(
self,
cost_function: Callable[[list[float]], float],
init_parameters: list[float],
bounds: list[tuple[float, float]],
store_intermediate_results: bool = False,
) -> OptimizerResult:
"""optimize the cost function and return the optimal parameters.
Args:
cost_function (Callable[[list[float]], float]): a function that takes in a list of parameters and returns the cost.
init_parameters (list[float]): the list of initial parameters. Note: the length of this list determines the number of parameters the optimizer will consider.
bounds (list[float, float]): a list of the variable value bounds.
Returns:
list[float]: the optimal set of parameters that minimize the cost function.
"""
intermediate_results: list[OptimizerIntermediateResult] = []
def callback_fun(intermediate_result: OptimizeResult) -> None:
# Create an OptimizerResult for this intermediate iteration.
intermediate_results.append(
OptimizerIntermediateResult(cost=intermediate_result.fun, parameters=intermediate_result.x.tolist())
)
# Only pass the callback if we want to store intermediate results.
callback = callback_fun if store_intermediate_results else None
# Global optimizer have a different interface, like `scipy.optimize.shgo` rather than `scipy.optimize.minimize`
if self.method in {"direct", "dual_annealing", "differential_evolution", "shgo"} and isinstance(
self.method, str
):
res = getattr(scipy_optimize, self.method)(
cost_function,
bounds=bounds,
callback=callback,
**self.extra_arguments,
)
# basinhopping doesn't allow bounds
elif self.method == "basinhopping":
res = scipy_optimize.basinhopping(
cost_function,
x0=init_parameters,
callback=callback,
**self.extra_arguments,
)
# the more general local minimizer interface
else:
res = scipy_optimize.minimize(
cost_function,
x0=init_parameters,
method=self.method,
bounds=bounds,
jac=self.extra_arguments.get("jac", None),
hess=self.extra_arguments.get("hess", None),
hessp=self.extra_arguments.get("hessp", None),
constraints=self.extra_arguments.get("constraints", ()),
tol=self.extra_arguments.get("tol", None),
options=self.extra_arguments.get("options", None),
callback=callback,
)
return OptimizerResult(
optimal_cost=res.fun,
optimal_parameters=res.x.tolist(),
intermediate_results=intermediate_results,
)
def __repr__(self) -> str:
extra_args_str = ", ".join(f"{key}={value!r}" for key, value in self.extra_arguments.items())
return f"SciPyOptimizer(method={self.method!r}, {extra_args_str})"
