pybop.optimisers.base_optimiser#
Classes#
A base class for defining optimisation methods. |
Module Contents#
- class pybop.optimisers.base_optimiser.BaseOptimiser(bounds=None)[source]#
A base class for defining optimisation methods.
This class serves as a template for creating optimisers. It provides a basic structure for an optimisation algorithm, including the initial setup and a method stub for performing the optimisation process. Child classes should override the optimise and _runoptimise methods with specific algorithms.
- _runoptimise(cost_function, x0=None)[source]#
Contains the logic for the optimisation algorithm.
This method should be implemented by child classes to perform the actual optimisation.
- Parameters:
cost_function (callable) – The cost function to be minimised by the optimiser.
x0 (ndarray, optional) – Initial guess for the parameters. Default is None.
- Returns:
This method is expected to return the result of the optimisation, the format of which
will be determined by the child class implementation.
- name()[source]#
Returns the name of the optimiser.
- Returns:
The name of the optimiser, which is “BaseOptimiser” for this base class.
- Return type:
str
- optimise(cost_function, x0=None, maxiter=None)[source]#
Initiates the optimisation process.
This method should be overridden by child classes with the specific optimisation algorithm.
- Parameters:
cost_function (callable) – The cost function to be minimised by the optimiser.
x0 (ndarray, optional) – Initial guess for the parameters. Default is None.
maxiter (int, optional) – Maximum number of iterations to perform. Default is None.
- Return type:
The result of the optimisation process. The specific type of this result will depend on the child implementation.