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Optimizer
class Optimizer
Bases: abc.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
get_support_level
abstract Optimizer.get_support_level()
Return support level dictionary
gradient_num_diff
static Optimizer.gradient_num_diff(x_center, f, epsilon, max_evals_grouped=None)
We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
Parameters
- x_center (ndarray) – point around which we compute the gradient
- f (func) – the function of which the gradient is to be computed.
- epsilon (float) – the epsilon used in the numeric differentiation.
- max_evals_grouped (int) – max evals grouped, defaults to 1 (i.e. no batching).
Returns
the gradient computed
Return type
grad
minimize
abstract Optimizer.minimize(fun, x0, jac=None, bounds=None)
Minimize the scalar function.
Parameters
- fun (
Callable
[[Union
[float
,ndarray
]],float
]) – The scalar function to minimize. - x0 (
Union
[float
,ndarray
]) – The initial point for the minimization. - jac (
Optional
[Callable
[[Union
[float
,ndarray
]],Union
[float
,ndarray
]]]) – The gradient of the scalar functionfun
. - bounds (
Optional
[List
[Tuple
[float
,float
]]]) – Bounds for the variables offun
. This argument might be ignored if the optimizer does not support bounds.
Return type
Returns
The result of the optimization, containing e.g. the result as attribute x
.
print_options
Optimizer.print_options()
Print algorithm-specific options.
set_max_evals_grouped
Optimizer.set_max_evals_grouped(limit)
Set max evals grouped
set_options
Optimizer.set_options(**kwargs)
Sets or updates values in the options dictionary.
The options dictionary may be used internally by a given optimizer to pass additional optional values for the underlying optimizer/optimization function used. The options dictionary may be initially populated with a set of key/values when the given optimizer is constructed.
Parameters
kwargs (dict) – options, given as name=value.
wrap_function
static Optimizer.wrap_function(function, args)
Wrap the function to implicitly inject the args at the call of the function.
Parameters
- function (func) – the target function
- args (tuple) – the args to be injected
Returns
wrapper
Return type
function_wrapper
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)
Return type
Dict
[str
, Any
]
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