From 39e46c8bc8fec1c9202bfc3136438b9da985ccb8 Mon Sep 17 00:00:00 2001
From: Janos Gabler <janos.gabler@gmail.com>
Date: Sun, 17 Aug 2025 15:57:08 +0200
Subject: [PATCH 1/2] Add algorithm classes.

---
 src/optimini/algorithms.py | 78 ++++++++++++++++++++++++++++++++++++++
 src/optimini/minimize.py   | 14 ++-----
 src/optimini/utils.py      | 16 ++++++++
 3 files changed, 98 insertions(+), 10 deletions(-)
 create mode 100644 src/optimini/algorithms.py

diff --git a/src/optimini/algorithms.py b/src/optimini/algorithms.py
new file mode 100644
index 0000000..9f84562
--- /dev/null
+++ b/src/optimini/algorithms.py
@@ -0,0 +1,78 @@
+from dataclasses import dataclass
+
+import numpy as np
+from numpy.typing import NDArray
+from scipy.optimize import minimize as scipy_minimize
+
+from optimini.internal_problem import InternalProblem
+from optimini.utils import Algorithm, InternalResult
+
+
+@dataclass(frozen=True)
+class SciPyLBFGSB(Algorithm):
+    convergence_ftol: float = 1e-8
+    stopping_maxiter: int = 10_000
+    limited_memory_length: int = 12
+    # more options here ...
+
+    def _solve_internal_problem(
+        self, problem: InternalProblem, x0: NDArray[np.float64]
+    ) -> InternalResult:
+        options = {
+            "maxcor": self.limited_memory_length,
+            "ftol": self.convergence_ftol,
+            "maxiter": self.stopping_maxiter,
+        }
+        res = scipy_minimize(
+            fun=problem.fun,
+            x0=x0,
+            method="L-BFGS-B",
+            options=options,
+        )
+        return InternalResult(x=res.x, fun=res.fun)
+
+
+@dataclass(frozen=True)
+class SciPyCG(Algorithm):
+    convergence_gtol: float = 1e-8
+    stopping_maxiter: int = 10_000
+    # more options here ...
+
+    def _solve_internal_problem(
+        self, problem: InternalProblem, x0: NDArray[np.float64]
+    ) -> InternalResult:
+        options = {
+            "gtol": self.convergence_gtol,
+            "maxiter": self.stopping_maxiter,
+        }
+        res = scipy_minimize(fun=problem.fun, x0=x0, method="CG", options=options)
+        return InternalResult(x=res.x, fun=res.fun)
+
+
+@dataclass(frozen=True)
+class SciPyNelderMead(Algorithm):
+    stopping_maxiter: int = 10_000
+    convergence_ftol: float = 1e-8
+    adaptive: bool = True
+    # more options here ...
+
+    def _solve_internal_problem(
+        self, problem: InternalProblem, x0: NDArray[np.float64]
+    ) -> InternalResult:
+        options = {
+            "maxiter": self.stopping_maxiter,
+            "ftol": self.convergence_ftol,
+            "adaptive": self.adaptive,
+        }
+
+        res = scipy_minimize(
+            fun=problem.fun, x0=x0, method="Nelder-Mead", options=options
+        )
+        return InternalResult(x=res.x, fun=res.fun)
+
+
+OPTIMIZER_REGISTRY = {
+    "L-BFGS-B": SciPyLBFGSB,
+    "CG": SciPyCG,
+    "Nelder-Mead": SciPyNelderMead,
+}
diff --git a/src/optimini/minimize.py b/src/optimini/minimize.py
index 8e19cd6..4607995 100644
--- a/src/optimini/minimize.py
+++ b/src/optimini/minimize.py
@@ -1,5 +1,4 @@
-from scipy.optimize import minimize as scipy_minimize
-
+from optimini.algorithms import OPTIMIZER_REGISTRY
 from optimini.converter import Converter
 from optimini.history import History
 from optimini.internal_problem import InternalProblem
@@ -7,18 +6,13 @@
 
 
 def minimize(fun, params, method, options=None):
-    """Minimize a function using a given method"""
     options = {} if options is None else options
     converter = Converter(params)
     history = History()
-    internal_fun = InternalProblem(fun, converter, history)
+    problem = InternalProblem(fun, converter, history)
     x0 = converter.flatten(params)
-    raw_res = scipy_minimize(
-        fun=internal_fun.fun,
-        x0=x0,
-        method=method,
-        options=options,
-    )
+    algo = OPTIMIZER_REGISTRY[method](**options)
+    raw_res = algo._solve_internal_problem(problem, x0)
     res = OptimizeResult(
         x=converter.unflatten(raw_res.x),
         history=history,
diff --git a/src/optimini/utils.py b/src/optimini/utils.py
index f08a8d4..210d407 100644
--- a/src/optimini/utils.py
+++ b/src/optimini/utils.py
@@ -1,9 +1,11 @@
+from abc import ABC, abstractmethod
 from dataclasses import dataclass
 
 import numpy as np
 from numpy.typing import NDArray
 
 from optimini.history import History
+from optimini.internal_problem import InternalProblem
 
 
 @dataclass
@@ -13,3 +15,17 @@ class OptimizeResult:
     x: dict | NDArray[np.float64]
     history: History
     fun: float
+
+
+@dataclass(frozen=True)
+class InternalResult:
+    x: NDArray[np.float64]
+    fun: float
+
+
+class Algorithm(ABC):
+    @abstractmethod
+    def _solve_internal_problem(
+        self, problem: InternalProblem, x0: NDArray[np.float64]
+    ) -> InternalResult:
+        pass

From a54c39904d8f01a690af83d213e10066c4f84a75 Mon Sep 17 00:00:00 2001
From: Janos Gabler <janos.gabler@gmail.com>
Date: Sun, 17 Aug 2025 16:52:27 +0200
Subject: [PATCH 2/2] Fix Nelder Mead

---
 src/optimini/algorithms.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/optimini/algorithms.py b/src/optimini/algorithms.py
index 9f84562..cc8df19 100644
--- a/src/optimini/algorithms.py
+++ b/src/optimini/algorithms.py
@@ -61,7 +61,7 @@ def _solve_internal_problem(
     ) -> InternalResult:
         options = {
             "maxiter": self.stopping_maxiter,
-            "ftol": self.convergence_ftol,
+            "fatol": self.convergence_ftol,
             "adaptive": self.adaptive,
         }