Add Nelder-Mead optimiser from PINTS

CHANGELOG.md

@@ -2,6 +2,7 @@
 
 ## Features
 
+- [#195](https://github.com/pybop-team/PyBOP/issues/195) - Adds the Nelder-Mead optimiser from PINTS as another option.
 
 ## Bug Fixes
 

examples/scripts/spm_NelderMead.py

@@ -0,0 +1,82 @@
+import numpy as np
+
+import pybop
+
+# Parameter set and model definition
+parameter_set = pybop.ParameterSet.pybamm("Chen2020")
+model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
+
+# Fitting parameters
+parameters = [
+    pybop.Parameter(
+        "Negative electrode active material volume fraction",
+        prior=pybop.Gaussian(0.68, 0.05),
+    ),
+    pybop.Parameter(
+        "Positive electrode active material volume fraction",
+        prior=pybop.Gaussian(0.58, 0.05),
+    ),
+]
+
+# Generate data
+init_soc = 0.5
+sigma = 0.003
+experiment = pybop.Experiment(
+    [
+        (
+            "Discharge at 0.5C for 3 minutes (1 second period)",
+            "Charge at 0.5C for 3 minutes (1 second period)",
+        ),
+    ]
+    * 2
+)
+values = model.predict(init_soc=init_soc, experiment=experiment)
+
+
+def noise(sigma):
+    return np.random.normal(0, sigma, len(values["Voltage [V]"].data))
+
+
+# Form dataset
+dataset = pybop.Dataset(
+    {
+        "Time [s]": values["Time [s]"].data,
+        "Current function [A]": values["Current [A]"].data,
+        "Voltage [V]": values["Voltage [V]"].data + noise(sigma),
+        "Bulk open-circuit voltage [V]": values["Bulk open-circuit voltage [V]"].data
+        + noise(sigma),
+    }
+)
+
+signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"]
+# Generate problem, cost function, and optimisation class
+problem = pybop.FittingProblem(
+    model, parameters, dataset, signal=signal, init_soc=init_soc
+)
+cost = pybop.RootMeanSquaredError(problem)
+optim = pybop.Optimisation(
+    cost,
+    optimiser=pybop.NelderMead,
+    verbose=True,
+    allow_infeasible_solutions=True,
+    sigma0=0.05,
+)
+optim.set_max_iterations(100)
+optim.set_max_unchanged_iterations(45)
+
+# Run optimisation
+x, final_cost = optim.run()
+print("Estimated parameters:", x)
+
+# Plot the timeseries output
+pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison")
+
+# Plot convergence
+pybop.plot_convergence(optim)
+
+# Plot the parameter traces
+pybop.plot_parameters(optim)
+
+# Plot the cost landscape with optimisation path
+bounds = np.array([[0.5, 0.8], [0.4, 0.7]])
+pybop.plot2d(optim, bounds=bounds, steps=15)

pybop/__init__.py

@@ -88,6 +88,7 @@
     Adam,
     CMAES,
     IRPropMin,
+    NelderMead,
     PSO,
     SNES,
     XNES,

pybop/optimisers/pints_optimisers.py

@@ -16,7 +16,7 @@ class GradientDescent(pints.GradientDescent):
         Initial position from which optimization will start.
     sigma0 : float, optional
         Initial step size (default is 0.1).
-    bounds : sequence or ``Bounds``, optional
+    bounds : dict, optional
         Ignored by this optimiser, provided for API consistency.
 
     See Also
@@ -46,7 +46,7 @@ class Adam(pints.Adam):
         Initial position from which optimization will start.
     sigma0 : float, optional
         Initial step size (default is 0.1).
-    bounds : sequence or ``Bounds``, optional
+    bounds : dict, optional
         Ignored by this optimiser, provided for API consistency.
 
     See Also
@@ -77,7 +77,8 @@ class IRPropMin(pints.IRPropMin):
     sigma0 : float, optional
         Initial step size (default is 0.1).
     bounds : dict, optional
-        Lower and upper bounds for each optimization parameter.
+        A dictionary with 'lower' and 'upper' keys containing arrays for lower and upper
+        bounds on the parameters.
 
     See Also
     --------
@@ -109,7 +110,8 @@ class PSO(pints.PSO):
     sigma0 : float, optional
         Spread of the initial particle positions (default is 0.1).
     bounds : dict, optional
-        Lower and upper bounds for each optimization parameter.
+        A dictionary with 'lower' and 'upper' keys containing arrays for lower and upper
+        bounds on the parameters.
 
     See Also
     --------
@@ -147,7 +149,8 @@ class SNES(pints.SNES):
     sigma0 : float, optional
         Initial standard deviation of the sampling distribution, defaults to 0.1.
     bounds : dict, optional
-        Lower and upper bounds for each optimization parameter.
+        A dictionary with 'lower' and 'upper' keys containing arrays for lower and upper
+        bounds on the parameters.
 
     See Also
     --------
@@ -168,7 +171,9 @@ class XNES(pints.XNES):
     """
     Implements the Exponential Natural Evolution Strategy (XNES) optimiser from PINTS.
 
-    XNES is an evolutionary algorithm that samples from a multivariate normal distribution, which is updated iteratively to fit the distribution of successful solutions.
+    XNES is an evolutionary algorithm that samples from a multivariate normal
+    distribution, which is updated iteratively to fit the distribution of successful
+    solutions.
 
     Parameters
     ----------
@@ -177,7 +182,8 @@ class XNES(pints.XNES):
     sigma0 : float, optional
         Initial standard deviation of the sampling distribution, defaults to 0.1.
     bounds : dict, optional
-        A dictionary with 'lower' and 'upper' keys containing arrays for lower and upper bounds on the parameters. If ``None``, no bounds are enforced.
+        A dictionary with 'lower' and 'upper' keys containing arrays for lower and upper
+        bounds on the parameters. If ``None``, no bounds are enforced.
 
     See Also
     --------
@@ -194,12 +200,44 @@ def __init__(self, x0, sigma0=0.1, bounds=None):
         super().__init__(x0, sigma0, self.boundaries)
 
 
+class NelderMead(pints.NelderMead):
+    """
+    Implements the Nelder-Mead downhill simplex method from PINTS.
+
+    This is a deterministic local optimiser. In most update steps it performs
+    either one evaluation, or two sequential evaluations, so that it will not
+    typically benefit from parallelisation.
+
+    Parameters
+    ----------
+    x0 : array_like
+        The initial parameter vector to optimize.
+    sigma0 : float, optional
+        Initial standard deviation of the sampling distribution, defaults to 0.1.
+        Does not appear to be used.
+    bounds : dict, optional
+        Ignored by this optimiser, provided for API consistency.
+
+    See Also
+    --------
+    pints.NelderMead : PINTS implementation of Nelder-Mead algorithm.
+    """
+
+    def __init__(self, x0, sigma0=0.1, bounds=None):
+        if bounds is not None:
+            print("NOTE: Boundaries ignored by NelderMead")
+
+        self.boundaries = None  # Bounds ignored in pints.NelderMead
+        super().__init__(x0, sigma0, self.boundaries)
+
+
 class CMAES(pints.CMAES):
     """
     Adapter for the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimiser in PINTS.
 
     CMA-ES is an evolutionary algorithm for difficult non-linear non-convex optimization problems.
-    It adapts the covariance matrix of a multivariate normal distribution to capture the shape of the cost landscape.
+    It adapts the covariance matrix of a multivariate normal distribution to capture the shape of
+    the cost landscape.
 
     Parameters
     ----------
@@ -208,8 +246,8 @@ class CMAES(pints.CMAES):
     sigma0 : float, optional
         Initial standard deviation of the sampling distribution, defaults to 0.1.
     bounds : dict, optional
-        A dictionary with 'lower' and 'upper' keys containing arrays for lower and upper bounds on the parameters.
-        If ``None``, no bounds are enforced.
+        A dictionary with 'lower' and 'upper' keys containing arrays for lower and upper
+        bounds on the parameters. If ``None``, no bounds are enforced.
 
     See Also
     --------

tests/integration/test_parameterisations.py

@@ -84,6 +84,7 @@ def spm_costs(self, model, parameters, cost_class, init_soc):
             pybop.CMAES,
             pybop.GradientDescent,
             pybop.IRPropMin,
+            pybop.NelderMead,
             pybop.PSO,
             pybop.SNES,
             pybop.XNES,

tests/unit/test_optimisation.py

@@ -80,6 +80,7 @@ def two_param_cost(self, model, two_parameters, dataset):
             (pybop.XNES, "Exponential Natural Evolution Strategy (xNES)"),
             (pybop.PSO, "Particle Swarm Optimisation (PSO)"),
             (pybop.IRPropMin, "iRprop-"),
+            (pybop.NelderMead, "Nelder-Mead"),
         ],
     )
     @pytest.mark.unit