pybop.optimisers.scipy_optimisers#

Classes#

`SciPyDifferentialEvolution`	Adapts SciPy's differential_evolution function for global optimization.
`SciPyMinimize`	Adapts SciPy's minimize function for use as an optimization strategy.

Module Contents#

class pybop.optimisers.scipy_optimisers.SciPyDifferentialEvolution(bounds=None, strategy='best1bin', maxiter=1000, popsize=15, tol=1e-05)[source]#

Bases: pybop.optimisers.base_optimiser.BaseOptimiser

Adapts SciPy’s differential_evolution function for global optimization.

This class provides a global optimization strategy based on differential evolution, useful for problems involving continuous parameters and potentially multiple local minima.

Parameters:

bounds (sequence or Bounds) – Bounds for variables. Must be provided as it is essential for differential evolution.
strategy (str, optional) – The differential evolution strategy to use. Defaults to ‘best1bin’.
maxiter (int, optional) – Maximum number of iterations to perform. Defaults to 1000.
popsize (int, optional) – The number of individuals in the population. Defaults to 15.

_runoptimise(cost_function, x0=None)[source]#

Executes the optimization process using SciPy’s differential_evolution function.

Parameters:

cost_function (callable) – The objective function to minimize.
x0 (array_like, optional) – Ignored parameter, provided for API consistency.

Returns:

A tuple (x, final_cost) containing the optimized parameters and the value of cost_function at the optimum.

Return type:

tuple

name()[source]#

Provides the name of the optimization strategy.

Returns:: The name ‘SciPyDifferentialEvolution’.
Return type:: str

needs_sensitivities()[source]#

Determines if the optimization algorithm requires gradient information.

Returns:: False, indicating that gradient information is not required for differential evolution.
Return type:: bool

set_population_size(population_size=None)[source]#: Sets a population size to use in this optimisation. Credit: PINTS

class pybop.optimisers.scipy_optimisers.SciPyMinimize(method=None, bounds=None, maxiter=None, tol=1e-05)[source]#

Bases: pybop.optimisers.base_optimiser.BaseOptimiser

Adapts SciPy’s minimize function for use as an optimization strategy.

This class provides an interface to various scalar minimization algorithms implemented in SciPy, allowing fine-tuning of the optimization process through method selection and option configuration.

Parameters:

method (str, optional) – The type of solver to use. If not specified, defaults to ‘Nelder-Mead’. Options: ‘Nelder-Mead’, ‘Powell’, ‘CG’, ‘BFGS’, ‘Newton-CG’, ‘L-BFGS-B’, ‘TNC’, ‘COBYLA’, ‘SLSQP’, ‘trust-constr’, ‘dogleg’, ‘trust-ncg’, ‘trust-exact’, ‘trust-krylov’.
bounds (sequence or Bounds, optional) – Bounds for variables as supported by the selected method.
maxiter (int, optional) – Maximum number of iterations to perform.

_runoptimise(cost_function, x0)[source]#

Executes the optimization process using SciPy’s minimize function.

Parameters:

cost_function (callable) – The objective function to minimize.
x0 (array_like) – Initial guess for the parameters.

Returns:

A tuple (x, final_cost) containing the optimized parameters and the value of cost_function at the optimum.

Return type:

tuple

name()[source]#

Provides the name of the optimization strategy.

Returns:: The name ‘SciPyMinimize’.
Return type:: str

needs_sensitivities()[source]#

Determines if the optimization algorithm requires gradient information.

Returns:: False, indicating that gradient information is not required.
Return type:: bool