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# Optimizer

class Optimizer

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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

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

Return type

### 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 function fun.
• bounds (Optional[List[Tuple[float, float]]]) – Bounds for the variables of fun. This argument might be ignored if the optimizer does not support bounds.

Return type

OptimizerResult

Returns

The result of the optimization, containing e.g. the result as attribute x.

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

### 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_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

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]