docs

Author: gauravmanmode

docs/source/algorithms.md

@@ -4710,14 +4710,7 @@ 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
+.. dropdown:: gfo_pso
 
   **How to use this algorithm.**
 
@@ -4727,7 +4720,7 @@ package are available in optimagic. To use it, you need to have
     om.minimize(
       fun=lambda x: x @ x,
       params=[1.0, 2.0, 3.0],
-      algorithm=om.algos.gfo_hillclimbing(stopping_maxiter=1_000, ...),
+      algorithm=om.algos.gfo_pso(stopping_maxiter=1_000, ...),
       bounds = om.Bounds(lower = np.array([1,1,1]), upper=np.array([5,5,5]))
     )
 
@@ -4738,62 +4731,31 @@ package are available in optimagic. To use it, you need to have
     om.minimize(
       fun=lambda x: x @ x,
       params=[1.0, 2.0, 3.0],
-      algorithm="gfo_hillclimbing",
+      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.GFOHillClimbing
+  .. autoclass:: optimagic.optimizers.gfo_optimizers.GFOParticleSwarmOptimization
 
 ```
 
 ```{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
+.. dropdown:: gfo_parallel_tempering
 
   **How to use this algorithm.**
 
   .. code-block:: python
 
     import optimagic as om
+    import numpy as np
     om.minimize(
       fun=lambda x: x @ x,
-      params=[1.0, 2.0, 3.0],
-      algorithm=om.algos.gfo_repulsinghillclimbing(stopping_maxiter=1_000, ...),
+      params=np.array([1.0, 2.0, 3.0]),
+      algorithm=om.algos.gfo_parallel_tempering(population_size=15, n_iter_swap=5),
       bounds = om.Bounds(lower = np.array([1,1,1]), upper=np.array([5,5,5]))
     )
 
@@ -4803,30 +4765,30 @@ package are available in optimagic. To use it, you need to have
 
     om.minimize(
       fun=lambda x: x @ x,
-      params=[1.0, 2.0, 3.0],
-      algorithm="gfo_repulsinghillclimbing",
-      algo_options={"stopping_maxiter": 1_000, ...},
+      params=np.array([1.0, 2.0, 3.0]),
+      algorithm="gfo_parallel_tempering",
+      algo_options={"population_size": 15, "n_iter_swap": 5},
       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
-
+  .. autoclass:: optimagic.optimizers.gfo_optimizers.GFOParallelTempering
 ```
 
 ```{eval-rst}
-.. dropdown:: gfo_randomrestarthillclimbing
+.. dropdown:: gfo_spiral_optimization
 
   **How to use this algorithm.**
 
   .. code-block:: python
 
     import optimagic as om
+    import numpy as np
     om.minimize(
       fun=lambda x: x @ x,
-      params=[1.0, 2.0, 3.0],
-      algorithm=om.algos.gfo_randomrestarthillclimbing(stopping_maxiter=1_000, ...),
+      params=np.array([1.0, 2.0, 3.0]),
+      algorithm=om.algos.gfo_spiral_optimization(population_size=15, decay_rate=0.95),
       bounds = om.Bounds(lower = np.array([1,1,1]), upper=np.array([5,5,5]))
     )
 
@@ -4836,30 +4798,30 @@ package are available in optimagic. To use it, you need to have
 
     om.minimize(
       fun=lambda x: x @ x,
-      params=[1.0, 2.0, 3.0],
-      algorithm="gfo_randomrestarthillclimbing",
-      algo_options={"stopping_maxiter": 1_000, ...},
+      params=np.array([1.0, 2.0, 3.0]),
+      algorithm="gfo_spiral_optimization",
+      algo_options={"population_size": 15, "decay_rate": 0.95},
       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
-
+  .. autoclass:: optimagic.optimizers.gfo_optimizers.GFOSpiralOptimization
 ```
 
 ```{eval-rst}
-.. dropdown:: gfo_simulatedannealing
+.. dropdown:: gfo_genetic_algorithm
 
   **How to use this algorithm.**
 
   .. code-block:: python
 
     import optimagic as om
+    import numpy as np
     om.minimize(
       fun=lambda x: x @ x,
-      params=[1.0, 2.0, 3.0],
-      algorithm=om.algos.gfo_simulatedannealing(stopping_maxiter=1_000, ...),
+      params=np.array([1.0, 2.0, 3.0]),
+      algorithm=om.algos.gfo_genetic_algorithm(population_size=20, mutation_rate=0.6),
       bounds = om.Bounds(lower = np.array([1,1,1]), upper=np.array([5,5,5]))
     )
 
@@ -4869,30 +4831,30 @@ package are available in optimagic. To use it, you need to have
 
     om.minimize(
       fun=lambda x: x @ x,
-      params=[1.0, 2.0, 3.0],
-      algorithm="gfo_simulatedannealing",
-      algo_options={"stopping_maxiter": 1_000, ...},
+      params=np.array([1.0, 2.0, 3.0]),
+      algorithm="gfo_genetic_algorithm",
+      algo_options={"population_size": 20, "mutation_rate": 0.6},
       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
-
+  .. autoclass:: optimagic.optimizers.gfo_optimizers.GFOGeneticAlgorithm
 ```
 
 ```{eval-rst}
-.. dropdown:: gfo_downhillsimplex
+.. dropdown:: gfo_evolution_strategy
 
   **How to use this algorithm.**
 
   .. code-block:: python
 
     import optimagic as om
+    import numpy as np
     om.minimize(
       fun=lambda x: x @ x,
-      params=[1.0, 2.0, 3.0],
-      algorithm=om.algos.gfo_downhillsimplex(stopping_maxiter=1_000, ...),
+      params=np.array([1.0, 2.0, 3.0]),
+      algorithm=om.algos.gfo_evolution_strategy(population_size=15, crossover_rate=0.4),
       bounds = om.Bounds(lower = np.array([1,1,1]), upper=np.array([5,5,5]))
     )
 
@@ -4902,30 +4864,30 @@ package are available in optimagic. To use it, you need to have
 
     om.minimize(
       fun=lambda x: x @ x,
-      params=[1.0, 2.0, 3.0],
-      algorithm="gfo_downhillsimplex",
-      algo_options={"stopping_maxiter": 1_000, ...},
+      params=np.array([1.0, 2.0, 3.0]),
+      algorithm="gfo_evolution_strategy",
+      algo_options={"population_size": 15, "crossover_rate": 0.4},
       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
-
+  .. autoclass:: optimagic.optimizers.gfo_optimizers.GFOEvolutionStrategy
 ```
 
 ```{eval-rst}
-.. dropdown:: gfo_pso
+.. dropdown:: gfo_differential_evolution
 
   **How to use this algorithm.**
 
   .. code-block:: python
 
     import optimagic as om
+    import numpy as np
     om.minimize(
       fun=lambda x: x @ x,
-      params=[1.0, 2.0, 3.0],
-      algorithm=om.algos.gfo_pso(stopping_maxiter=1_000, ...),
+      params=np.array([1.0, 2.0, 3.0]),
+      algorithm=om.algos.gfo_differential_evolution(population_size=20, mutation_rate=0.8),
       bounds = om.Bounds(lower = np.array([1,1,1]), upper=np.array([5,5,5]))
     )
 
@@ -4935,15 +4897,15 @@ package are available in optimagic. To use it, you need to have
 
     om.minimize(
       fun=lambda x: x @ x,
-      params=[1.0, 2.0, 3.0],
-      algorithm="gfo_pso",
-      algo_options={"stopping_maxiter": 1_000, ...},
+      params=np.array([1.0, 2.0, 3.0]),
+      algorithm="gfo_differential_evolution",
+      algo_options={"population_size": 20, "mutation_rate": 0.8},
       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
+  .. autoclass:: optimagic.optimizers.gfo_optimizers.GFODifferentialEvolution
 
 ```