[Opt] Exposed Powell and NelderMead gradient-free optimizers

CHANGELOG.md

@@ -9,6 +9,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
+- Wrapped two gradient-free optimizers from scipy: Nelder-Mead and Powell. They are available as `jax_fdm.optimizers.NelderMead` and `jax_fdm.optimizers.Powell`, respectively. 
+- Linked two evolutionary optimizers from scipy They are available as `jax_fdm.optimizers.DualAnnealing` and `jax_fdm.optimizers.DifferentialEvolution`.
 - Added support for kwargs in `LossPlotter.plot()`. The kwargs control the parameters of the equilibrium model used to plot the loss history.
 - Added `VertexSupportParameter.index()`. This change might appear redundant, but it was necessary to deal with the method resolution order of the parent classes of `VertexSupportParameter`.
 - Added `VertexGroupSupportParameter.index()` for similar reasons as the listed above.

src/jax_fdm/optimization/optimizers/gradient_free.py

@@ -0,0 +1,24 @@
+"""
+A collection of scipy-powered, gradient-free optimizers.
+"""
+from jax_fdm.optimization.optimizers import Optimizer
+
+
+# ==========================================================================
+# Optimizers
+# ==========================================================================
+
+class Powell(Optimizer):
+    """
+    The modified Powell algorithm for gradient-free optimization with box constraints.
+    """
+    def __init__(self, **kwargs):
+        super().__init__(name="Powell", disp=0, **kwargs)
+
+
+class NelderMead(Optimizer):
+    """
+    The Nelder-Mead gradient-free optimizer with box constraints.
+    """
+    def __init__(self, **kwargs):
+        super().__init__(name="Nelder-Mead", disp=0, **kwargs)