Wrap gradient_free_optimizers (local)

.tools/envs/testenv-linux.yml

@@ -36,6 +36,7 @@ dependencies:
       - fides==0.7.4  # dev, tests
       - kaleido>=1.0  # dev, tests
       - bayes_optim  # dev, tests
+      - gradient_free_optimizers  # dev, tests
       - pandas-stubs  # dev, tests
       - types-cffi  # dev, tests
       - types-openpyxl  # dev, tests

.tools/envs/testenv-nevergrad.yml

@@ -33,12 +33,13 @@ dependencies:
       - fides==0.7.4  # dev, tests
       - kaleido>=1.0  # dev, tests
       - bayes_optim  # dev, tests
+      - gradient_free_optimizers  # dev, tests
       - pandas-stubs  # dev, tests
       - types-cffi  # dev, tests
       - types-openpyxl  # dev, tests
       - types-jinja2  # dev, tests
       - sqlalchemy-stubs  # dev, tests
       - sphinxcontrib-mermaid  # dev, tests, docs
-      - -e ../../
       - bayesian_optimization==1.4.0
       - nevergrad
+      - -e ../../

.tools/envs/testenv-numpy.yml

@@ -34,6 +34,7 @@ dependencies:
       - fides==0.7.4  # dev, tests
       - kaleido>=1.0  # dev, tests
       - bayes_optim  # dev, tests
+      - gradient_free_optimizers  # dev, tests
       - types-cffi  # dev, tests
       - types-openpyxl  # dev, tests
       - types-jinja2  # dev, tests

.tools/envs/testenv-others.yml

@@ -34,6 +34,7 @@ dependencies:
       - fides==0.7.4  # dev, tests
       - kaleido>=1.0  # dev, tests
       - bayes_optim  # dev, tests
+      - gradient_free_optimizers  # dev, tests
       - pandas-stubs  # dev, tests
       - types-cffi  # dev, tests
       - types-openpyxl  # dev, tests

.tools/envs/testenv-pandas.yml

@@ -34,6 +34,7 @@ dependencies:
       - fides==0.7.4  # dev, tests
       - kaleido>=1.0  # dev, tests
       - bayes_optim  # dev, tests
+      - gradient_free_optimizers  # dev, tests
       - types-cffi  # dev, tests
       - types-openpyxl  # dev, tests
       - types-jinja2  # dev, tests

.tools/envs/testenv-plotly.yml

@@ -33,11 +33,12 @@ dependencies:
       - Py-BOBYQA  # dev, tests
       - fides==0.7.4  # dev, tests
       - bayes_optim  # dev, tests
+      - gradient_free_optimizers  # dev, tests
       - pandas-stubs  # dev, tests
       - types-cffi  # dev, tests
       - types-openpyxl  # dev, tests
       - types-jinja2  # dev, tests
       - sqlalchemy-stubs  # dev, tests
       - sphinxcontrib-mermaid  # dev, tests, docs
-      - -e ../../
       - kaleido<0.3
+      - -e ../../

docs/source/algorithms.md

@@ -4699,6 +4699,252 @@ package. To use it, you need to have
       aligned structures and enhancing search performance in rotated coordinate systems. (Default:
       `False`)
     - **seed**: Seed for the random number generator for reproducibility.
+
+```
+
+## Gradient Free Optimizers
+
+Optimizers from the
+[gradient_free_optimizers](https://github.com/SimonBlanke/Gradient-Free-Optimizers?tab=readme-ov-file)
+package are available in optimagic. To use it, you need to have
+[gradient_free_optimizers](https://pypi.org/project/gradient_free_optimizers) installed.
+
+```{eval-rst}
+.. dropdown:: Common options across all optimizers
+
+  .. autoclass:: optimagic.optimizers.gradient_free_optimizers.GFOCommonOptions
+
+```
+
+```{eval-rst}
+.. dropdown:: gfo_hillclimbing
+
+  **How to use this algorithm.**
+
+  .. code-block:: python
+
+    import optimagic as om
+    om.minimize(
+      fun=lambda x: x @ x,
+      params=[1.0, 2.0, 3.0],
+      algorithm=om.algos.gfo_hillclimbing(stopping_maxiter=1_000, ...),
+      bounds = om.Bounds(lower = np.array([1,1,1]), upper=np.array([5,5,5]))
+    )
+
+  or using the string interface:
+
+  .. code-block:: python
+
+    om.minimize(
+      fun=lambda x: x @ x,
+      params=[1.0, 2.0, 3.0],
+      algorithm="gfo_hillclimbing",
+      algo_options={"stopping_maxiter": 1_000, ...},
+      bounds = om.Bounds(lower = np.array([1,1,1]), upper=np.array([5,5,5]))
+    )
+
+  **Description and available options:**
+
+  .. autoclass:: optimagic.optimizers.gradient_free_optimizers.GFOHillClimbing
+
+```
+
+```{eval-rst}
+.. dropdown:: gfo_stochastichillclimbing
+
+  **How to use this algorithm.**
+
+  .. code-block:: python
+
+    import optimagic as om
+    om.minimize(
+      fun=lambda x: x @ x,
+      params=[1.0, 2.0, 3.0],
+      algorithm=om.algos.gfo_stochastichillclimbing(stopping_maxiter=1_000, ...),
+      bounds = om.Bounds(lower = np.array([1,1,1]), upper=np.array([5,5,5]))
+    )
+
+  or using the string interface:
+
+  .. code-block:: python
+
+    om.minimize(
+      fun=lambda x: x @ x,
+      params=[1.0, 2.0, 3.0],
+      algorithm="gfo_stochastichillclimbing",
+      algo_options={"stopping_maxiter": 1_000, ...},
+      bounds = om.Bounds(lower = np.array([1,1,1]), upper=np.array([5,5,5]))
+    )
+
+  **Description and available options:**
+
+  .. autoclass:: optimagic.optimizers.gradient_free_optimizers.GFOStochasticHillClimbing
+
+```
+
+```{eval-rst}
+.. dropdown:: gfo_repulsinghillclimbing
+
+  **How to use this algorithm.**
+
+  .. code-block:: python
+
+    import optimagic as om
+    om.minimize(
+      fun=lambda x: x @ x,
+      params=[1.0, 2.0, 3.0],
+      algorithm=om.algos.gfo_repulsinghillclimbing(stopping_maxiter=1_000, ...),
+      bounds = om.Bounds(lower = np.array([1,1,1]), upper=np.array([5,5,5]))
+    )
+
+  or using the string interface:
+
+  .. code-block:: python
+
+    om.minimize(
+      fun=lambda x: x @ x,
+      params=[1.0, 2.0, 3.0],
+      algorithm="gfo_repulsinghillclimbing",
+      algo_options={"stopping_maxiter": 1_000, ...},
+      bounds = om.Bounds(lower = np.array([1,1,1]), upper=np.array([5,5,5]))
+    )
+
+  **Description and available options:**
+
+  .. autoclass:: optimagic.optimizers.gradient_free_optimizers.GFORepulsingHillClimbing
+
+```
+
+```{eval-rst}
+.. dropdown:: gfo_randomrestarthillclimbing
+
+  **How to use this algorithm.**
+
+  .. code-block:: python
+
+    import optimagic as om
+    om.minimize(
+      fun=lambda x: x @ x,
+      params=[1.0, 2.0, 3.0],
+      algorithm=om.algos.gfo_randomrestarthillclimbing(stopping_maxiter=1_000, ...),
+      bounds = om.Bounds(lower = np.array([1,1,1]), upper=np.array([5,5,5]))
+    )
+
+  or using the string interface:
+
+  .. code-block:: python
+
+    om.minimize(
+      fun=lambda x: x @ x,
+      params=[1.0, 2.0, 3.0],
+      algorithm="gfo_randomrestarthillclimbing",
+      algo_options={"stopping_maxiter": 1_000, ...},
+      bounds = om.Bounds(lower = np.array([1,1,1]), upper=np.array([5,5,5]))
+    )
+
+  **Description and available options:**
+
+  .. autoclass:: optimagic.optimizers.gradient_free_optimizers.GFORandomRestartHillClimbing
+
+```
+
+```{eval-rst}
+.. dropdown:: gfo_simulatedannealing
+
+  **How to use this algorithm.**
+
+  .. code-block:: python
+
+    import optimagic as om
+    om.minimize(
+      fun=lambda x: x @ x,
+      params=[1.0, 2.0, 3.0],
+      algorithm=om.algos.gfo_simulatedannealing(stopping_maxiter=1_000, ...),
+      bounds = om.Bounds(lower = np.array([1,1,1]), upper=np.array([5,5,5]))
+    )
+
+  or using the string interface:
+
+  .. code-block:: python
+
+    om.minimize(
+      fun=lambda x: x @ x,
+      params=[1.0, 2.0, 3.0],
+      algorithm="gfo_simulatedannealing",
+      algo_options={"stopping_maxiter": 1_000, ...},
+      bounds = om.Bounds(lower = np.array([1,1,1]), upper=np.array([5,5,5]))
+    )
+
+  **Description and available options:**
+
+  .. autoclass:: optimagic.optimizers.gradient_free_optimizers.GFOSimulatedAnnealing
+
+```
+
+```{eval-rst}
+.. dropdown:: gfo_downhillsimplex
+
+  **How to use this algorithm.**
+
+  .. code-block:: python
+
+    import optimagic as om
+    om.minimize(
+      fun=lambda x: x @ x,
+      params=[1.0, 2.0, 3.0],
+      algorithm=om.algos.gfo_downhillsimplex(stopping_maxiter=1_000, ...),
+      bounds = om.Bounds(lower = np.array([1,1,1]), upper=np.array([5,5,5]))
+    )
+
+  or using the string interface:
+
+  .. code-block:: python
+
+    om.minimize(
+      fun=lambda x: x @ x,
+      params=[1.0, 2.0, 3.0],
+      algorithm="gfo_downhillsimplex",
+      algo_options={"stopping_maxiter": 1_000, ...},
+      bounds = om.Bounds(lower = np.array([1,1,1]), upper=np.array([5,5,5]))
+    )
+
+  **Description and available options:**
+
+  .. autoclass:: optimagic.optimizers.gradient_free_optimizers.GFODownhillSimplex
+
+```
+
+```{eval-rst}
+.. dropdown:: gfo_pso
+
+  **How to use this algorithm.**
+
+  .. code-block:: python
+
+    import optimagic as om
+    om.minimize(
+      fun=lambda x: x @ x,
+      params=[1.0, 2.0, 3.0],
+      algorithm=om.algos.gfo_pso(stopping_maxiter=1_000, ...),
+      bounds = om.Bounds(lower = np.array([1,1,1]), upper=np.array([5,5,5]))
+    )
+
+  or using the string interface:
+
+  .. code-block:: python
+
+    om.minimize(
+      fun=lambda x: x @ x,
+      params=[1.0, 2.0, 3.0],
+      algorithm="gfo_pso",
+      algo_options={"stopping_maxiter": 1_000, ...},
+      bounds = om.Bounds(lower = np.array([1,1,1]), upper=np.array([5,5,5]))
+    )
+
+  **Description and available options:**
+
+  .. autoclass:: optimagic.optimizers.gradient_free_optimizers.GFOParticleSwarmOptimization
+
 ```
 
 ## References

environment.yml

@@ -48,6 +48,7 @@ dependencies:
       - kaleido>=1.0  # dev, tests
       - pre-commit>=4  # dev
       - bayes_optim  # dev, tests
+      - gradient_free_optimizers  # dev, tests
       - -e .  # dev
       # type stubs
       - pandas-stubs  # dev, tests

pyproject.toml

@@ -381,5 +381,7 @@ module = [
     "iminuit",
     "nevergrad",
     "yaml",
+    "gradient_free_optimizers",
+    "gradient_free_optimizers.optimizers.base_optimizer",
   ]
 ignore_missing_imports = true