added initial wrapper for migrad

.tools/envs/testenv-linux.yml

@@ -27,6 +27,7 @@ dependencies:
   - pyyaml  # dev, tests
   - jinja2  # dev, tests
   - annotated-types  # dev, tests
+  - iminuit  # dev, tests
   - pip:  # dev, tests, docs
       - DFO-LS>=1.5.3  # dev, tests
       - Py-BOBYQA  # dev, tests

.tools/envs/testenv-numpy.yml

@@ -25,6 +25,7 @@ dependencies:
   - pyyaml  # dev, tests
   - jinja2  # dev, tests
   - annotated-types  # dev, tests
+  - iminuit  # dev, tests
   - pip:  # dev, tests, docs
       - DFO-LS>=1.5.3  # dev, tests
       - Py-BOBYQA  # dev, tests

.tools/envs/testenv-others.yml

@@ -25,6 +25,7 @@ dependencies:
   - pyyaml  # dev, tests
   - jinja2  # dev, tests
   - annotated-types  # dev, tests
+  - iminuit  # dev, tests
   - pip:  # dev, tests, docs
       - DFO-LS>=1.5.3  # dev, tests
       - Py-BOBYQA  # dev, tests

.tools/envs/testenv-pandas.yml

@@ -25,6 +25,7 @@ dependencies:
   - pyyaml  # dev, tests
   - jinja2  # dev, tests
   - annotated-types  # dev, tests
+  - iminuit  # dev, tests
   - pip:  # dev, tests, docs
       - DFO-LS>=1.5.3  # dev, tests
       - Py-BOBYQA  # dev, tests

environment.yml

@@ -36,6 +36,7 @@ dependencies:
   - jinja2  # dev, tests
   - furo  # dev, docs
   - annotated-types  # dev, tests
+  - iminuit  # dev, tests
   - pip:  # dev, tests, docs
       - DFO-LS>=1.5.3  # dev, tests
       - Py-BOBYQA  # dev, tests

pyproject.toml

@@ -16,6 +16,7 @@ dependencies = [
     "sqlalchemy>=1.3",
     "annotated-types",
     "typing-extensions",
+    "iminuit",
 ]
 dynamic = ["version"]
 keywords = [

src/optimagic/algorithms.py

@@ -14,6 +14,7 @@
 from optimagic.optimization.algorithm import Algorithm
 from optimagic.optimizers.bhhh import BHHH
 from optimagic.optimizers.fides import Fides
+from optimagic.optimizers.iminuit_migrad import IminuitMigrad
 from optimagic.optimizers.ipopt import Ipopt
 from optimagic.optimizers.nag_optimizers import NagDFOLS, NagPyBOBYQA
 from optimagic.optimizers.neldermead import NelderMeadParallel
@@ -286,6 +287,7 @@ def Scalar(self) -> BoundedGradientBasedLocalNonlinearConstrainedScalarAlgorithm
 @dataclass(frozen=True)
 class BoundedGradientBasedLocalScalarAlgorithms(AlgoSelection):
     fides: Type[Fides] = Fides
+    iminuit_migrad: Type[IminuitMigrad] = IminuitMigrad
     ipopt: Type[Ipopt] = Ipopt
     nlopt_ccsaq: Type[NloptCCSAQ] = NloptCCSAQ
     nlopt_lbfgsb: Type[NloptLBFGSB] = NloptLBFGSB
@@ -840,6 +842,7 @@ def NonlinearConstrained(
 @dataclass(frozen=True)
 class BoundedGradientBasedLocalAlgorithms(AlgoSelection):
     fides: Type[Fides] = Fides
+    iminuit_migrad: Type[IminuitMigrad] = IminuitMigrad
     ipopt: Type[Ipopt] = Ipopt
     nlopt_ccsaq: Type[NloptCCSAQ] = NloptCCSAQ
     nlopt_lbfgsb: Type[NloptLBFGSB] = NloptLBFGSB
@@ -889,6 +892,7 @@ def Scalar(self) -> GradientBasedLocalNonlinearConstrainedScalarAlgorithms:
 @dataclass(frozen=True)
 class GradientBasedLocalScalarAlgorithms(AlgoSelection):
     fides: Type[Fides] = Fides
+    iminuit_migrad: Type[IminuitMigrad] = IminuitMigrad
     ipopt: Type[Ipopt] = Ipopt
     nlopt_ccsaq: Type[NloptCCSAQ] = NloptCCSAQ
     nlopt_lbfgsb: Type[NloptLBFGSB] = NloptLBFGSB
@@ -956,6 +960,7 @@ def Scalar(self) -> BoundedGradientBasedNonlinearConstrainedScalarAlgorithms:
 @dataclass(frozen=True)
 class BoundedGradientBasedScalarAlgorithms(AlgoSelection):
     fides: Type[Fides] = Fides
+    iminuit_migrad: Type[IminuitMigrad] = IminuitMigrad
     ipopt: Type[Ipopt] = Ipopt
     nlopt_ccsaq: Type[NloptCCSAQ] = NloptCCSAQ
     nlopt_lbfgsb: Type[NloptLBFGSB] = NloptLBFGSB
@@ -1674,6 +1679,7 @@ def Scalar(self) -> BoundedLocalNonlinearConstrainedScalarAlgorithms:
 @dataclass(frozen=True)
 class BoundedLocalScalarAlgorithms(AlgoSelection):
     fides: Type[Fides] = Fides
+    iminuit_migrad: Type[IminuitMigrad] = IminuitMigrad
     ipopt: Type[Ipopt] = Ipopt
     nag_pybobyqa: Type[NagPyBOBYQA] = NagPyBOBYQA
     nlopt_bobyqa: Type[NloptBOBYQA] = NloptBOBYQA
@@ -1943,6 +1949,7 @@ def Scalar(self) -> GlobalGradientBasedScalarAlgorithms:
 class GradientBasedLocalAlgorithms(AlgoSelection):
     bhhh: Type[BHHH] = BHHH
     fides: Type[Fides] = Fides
+    iminuit_migrad: Type[IminuitMigrad] = IminuitMigrad
     ipopt: Type[Ipopt] = Ipopt
     nlopt_ccsaq: Type[NloptCCSAQ] = NloptCCSAQ
     nlopt_lbfgsb: Type[NloptLBFGSB] = NloptLBFGSB
@@ -1985,6 +1992,7 @@ def Scalar(self) -> GradientBasedLocalScalarAlgorithms:
 @dataclass(frozen=True)
 class BoundedGradientBasedAlgorithms(AlgoSelection):
     fides: Type[Fides] = Fides
+    iminuit_migrad: Type[IminuitMigrad] = IminuitMigrad
     ipopt: Type[Ipopt] = Ipopt
     nlopt_ccsaq: Type[NloptCCSAQ] = NloptCCSAQ
     nlopt_lbfgsb: Type[NloptLBFGSB] = NloptLBFGSB
@@ -2054,6 +2062,7 @@ def Scalar(self) -> GradientBasedNonlinearConstrainedScalarAlgorithms:
 @dataclass(frozen=True)
 class GradientBasedScalarAlgorithms(AlgoSelection):
     fides: Type[Fides] = Fides
+    iminuit_migrad: Type[IminuitMigrad] = IminuitMigrad
     ipopt: Type[Ipopt] = Ipopt
     nlopt_ccsaq: Type[NloptCCSAQ] = NloptCCSAQ
     nlopt_lbfgsb: Type[NloptLBFGSB] = NloptLBFGSB
@@ -2577,6 +2586,7 @@ def Scalar(self) -> GlobalParallelScalarAlgorithms:
 @dataclass(frozen=True)
 class BoundedLocalAlgorithms(AlgoSelection):
     fides: Type[Fides] = Fides
+    iminuit_migrad: Type[IminuitMigrad] = IminuitMigrad
     ipopt: Type[Ipopt] = Ipopt
     nag_dfols: Type[NagDFOLS] = NagDFOLS
     nag_pybobyqa: Type[NagPyBOBYQA] = NagPyBOBYQA
@@ -2659,6 +2669,7 @@ def Scalar(self) -> LocalNonlinearConstrainedScalarAlgorithms:
 @dataclass(frozen=True)
 class LocalScalarAlgorithms(AlgoSelection):
     fides: Type[Fides] = Fides
+    iminuit_migrad: Type[IminuitMigrad] = IminuitMigrad
     ipopt: Type[Ipopt] = Ipopt
     nag_pybobyqa: Type[NagPyBOBYQA] = NagPyBOBYQA
     neldermead_parallel: Type[NelderMeadParallel] = NelderMeadParallel
@@ -2809,6 +2820,7 @@ def Scalar(self) -> BoundedNonlinearConstrainedScalarAlgorithms:
 @dataclass(frozen=True)
 class BoundedScalarAlgorithms(AlgoSelection):
     fides: Type[Fides] = Fides
+    iminuit_migrad: Type[IminuitMigrad] = IminuitMigrad
     ipopt: Type[Ipopt] = Ipopt
     nag_pybobyqa: Type[NagPyBOBYQA] = NagPyBOBYQA
     nlopt_bobyqa: Type[NloptBOBYQA] = NloptBOBYQA
@@ -3063,6 +3075,7 @@ def Local(self) -> LeastSquaresLocalParallelAlgorithms:
 class GradientBasedAlgorithms(AlgoSelection):
     bhhh: Type[BHHH] = BHHH
     fides: Type[Fides] = Fides
+    iminuit_migrad: Type[IminuitMigrad] = IminuitMigrad
     ipopt: Type[Ipopt] = Ipopt
     nlopt_ccsaq: Type[NloptCCSAQ] = NloptCCSAQ
     nlopt_lbfgsb: Type[NloptLBFGSB] = NloptLBFGSB
@@ -3246,6 +3259,7 @@ def Scalar(self) -> GlobalScalarAlgorithms:
 class LocalAlgorithms(AlgoSelection):
     bhhh: Type[BHHH] = BHHH
     fides: Type[Fides] = Fides
+    iminuit_migrad: Type[IminuitMigrad] = IminuitMigrad
     ipopt: Type[Ipopt] = Ipopt
     nag_dfols: Type[NagDFOLS] = NagDFOLS
     nag_pybobyqa: Type[NagPyBOBYQA] = NagPyBOBYQA
@@ -3316,6 +3330,7 @@ def Scalar(self) -> LocalScalarAlgorithms:
 @dataclass(frozen=True)
 class BoundedAlgorithms(AlgoSelection):
     fides: Type[Fides] = Fides
+    iminuit_migrad: Type[IminuitMigrad] = IminuitMigrad
     ipopt: Type[Ipopt] = Ipopt
     nag_dfols: Type[NagDFOLS] = NagDFOLS
     nag_pybobyqa: Type[NagPyBOBYQA] = NagPyBOBYQA
@@ -3451,6 +3466,7 @@ def Scalar(self) -> NonlinearConstrainedScalarAlgorithms:
 @dataclass(frozen=True)
 class ScalarAlgorithms(AlgoSelection):
     fides: Type[Fides] = Fides
+    iminuit_migrad: Type[IminuitMigrad] = IminuitMigrad
     ipopt: Type[Ipopt] = Ipopt
     nag_pybobyqa: Type[NagPyBOBYQA] = NagPyBOBYQA
     neldermead_parallel: Type[NelderMeadParallel] = NelderMeadParallel
@@ -3625,6 +3641,7 @@ def Scalar(self) -> ParallelScalarAlgorithms:
 class Algorithms(AlgoSelection):
     bhhh: Type[BHHH] = BHHH
     fides: Type[Fides] = Fides
+    iminuit_migrad: Type[IminuitMigrad] = IminuitMigrad
     ipopt: Type[Ipopt] = Ipopt
     nag_dfols: Type[NagDFOLS] = NagDFOLS
     nag_pybobyqa: Type[NagPyBOBYQA] = NagPyBOBYQA

src/optimagic/optimizers/iminuit_migrad.py

@@ -0,0 +1,102 @@
+from dataclasses import dataclass
+from typing import Callable, Optional
+
+import numpy as np
+from iminuit import Minuit  # type: ignore
+from numpy.typing import NDArray
+
+from optimagic import mark
+from optimagic.optimization.algo_options import (
+    STOPPING_MAXFUN,
+)
+from optimagic.optimization.algorithm import Algorithm, InternalOptimizeResult
+from optimagic.optimization.internal_optimization_problem import (
+    InternalOptimizationProblem,
+)
+from optimagic.typing import AggregationLevel
+
+
+@mark.minimizer(
+    name="iminuit_migrad",
+    solver_type=AggregationLevel.SCALAR,
+    is_available=True,
+    is_global=False,
+    needs_jac=True,
+    needs_hess=False,
+    supports_parallelism=False,
+    supports_bounds=True,
+    supports_linear_constraints=False,
+    supports_nonlinear_constraints=False,
+    disable_history=False,
+)
+@dataclass(frozen=True)
+class IminuitMigrad(Algorithm):
+    stopping_maxfun: int = STOPPING_MAXFUN
+
+    def _solve_internal_problem(
+        self, problem: InternalOptimizationProblem, params: NDArray[np.float64]
+    ) -> InternalOptimizeResult:
+        def wrapped_objective(x: NDArray[np.float64]) -> float:
+            return float(problem.fun(x))
+
+        wrapped_gradient = None
+        jac_func = None
+        if problem.jac is not None:
+
+            def wrapped_gradient(x: NDArray[np.float64]) -> NDArray[np.float64]:
+                return problem.jac(x)
+
+            jac_func = problem.jac
+        elif problem.fun_and_jac is not None:
+
+            def wrapped_gradient(x: NDArray[np.float64]) -> NDArray[np.float64]:
+                _, jac = problem.fun_and_jac(x)
+                return jac
+
+            jac_func = lambda x: problem.fun_and_jac(x)[1]
+
+        m = Minuit(wrapped_objective, params, grad=wrapped_gradient)
+
+        if problem.bounds:
+            lower_bounds = problem.bounds.lower
+            upper_bounds = problem.bounds.upper
+
+            if lower_bounds is not None and upper_bounds is not None:
+                for i, (lower, upper) in enumerate(
+                    zip(lower_bounds, upper_bounds, strict=False)
+                ):
+                    if lower is not None or upper is not None:
+                        m.limits[i] = (lower, upper)
+
+        m.migrad(ncall=self.stopping_maxfun)
+
+        res = process_minuit_result(m, jac_func)
+        return res
+
+
+def process_minuit_result(
+    minuit_result: Minuit,
+    jac_fun: Optional[Callable[[NDArray[np.float64]], NDArray[np.float64]]] = None,
+) -> InternalOptimizeResult:
+    jac = None
+    if jac_fun is not None:
+        jac = jac_fun(np.array(minuit_result.values))
+
+    res = InternalOptimizeResult(
+        x=np.array(minuit_result.values),
+        fun=minuit_result.fval,
+        success=minuit_result.valid,
+        message=repr(minuit_result.fmin),
+        n_fun_evals=minuit_result.nfcn,
+        n_jac_evals=minuit_result.ngrad,
+        n_hess_evals=None,
+        n_iterations=None,
+        status=None,
+        jac=jac,
+        hess=np.array(minuit_result.hesse()),
+        hess_inv=np.array(minuit_result.covariance),
+        max_constraint_violation=None,
+        info={"minos": minuit_result.minos()},
+        history=None,
+    )
+    return res