Optimizer¶
- class Optimizer[source]¶
Bases:
ABC
Base class for optimization algorithm.
Initialize the optimization algorithm, setting the support level for _gradient_support_level, _bound_support_level, _initial_point_support_level, and empty options.
Methods
Return support level dictionary
We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
Minimize the scalar function.
Print algorithm-specific options.
Set max evals grouped
Sets or updates values in the options dictionary.
Wrap the function to implicitly inject the args at the call of the function.
Attributes
- bounds_support_level¶
Returns bounds support level
- gradient_support_level¶
Returns gradient support level
- initial_point_support_level¶
Returns initial point support level
- is_bounds_ignored¶
Returns is bounds ignored
- is_bounds_required¶
Returns is bounds required
- is_bounds_supported¶
Returns is bounds supported
- is_gradient_ignored¶
Returns is gradient ignored
- is_gradient_required¶
Returns is gradient required
- is_gradient_supported¶
Returns is gradient supported
- is_initial_point_ignored¶
Returns is initial point ignored
- is_initial_point_required¶
Returns is initial point required
- is_initial_point_supported¶
Returns is initial point supported
- setting¶
Return setting
- settings¶
The optimizer settings in a dictionary format.
The settings can for instance be used for JSON-serialization (if all settings are serializable, which e.g. doesn’t hold per default for callables), such that the optimizer object can be reconstructed as
settings = optimizer.settings # JSON serialize and send to another server optimizer = OptimizerClass(**settings)