P_BFGS¶

class P_BFGS(maxfun=1000, ftol=np.float64(2.220446049250313e-15), iprint=-1, max_processes=None, options=None, max_evals_grouped=1, **kwargs)[source]¶

Bases: SciPyOptimizer

Parallelized Limited-memory BFGS optimizer.

P-BFGS is a parallelized version of L_BFGS_B with which it shares the same parameters. P-BFGS can be useful when the target hardware is a quantum simulator running on a classical machine. This allows the multiple processes to use simulation to potentially reach a minimum faster. The parallelization may also help the optimizer avoid getting stuck at local optima.

Uses scipy.optimize.fmin_l_bfgs_b. For further detail, please refer to https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin_l_bfgs_b.html

Note

This component has some function that is normally random. If you want to reproduce behavior then you should set the random number generator seed in the algorithm_globals (qiskit_machine_learning.utils.algorithm_globals.random_seed = seed).

Parameters:

maxfun (int) – Maximum number of function evaluations.
ftol (SupportsFloat) – The iteration stops when (f^k - f^{k+1})/max{|f^k|,|f^{k+1}|,1} <= ftol.
iprint (int) – Controls the frequency of output. iprint < 0 means no output; iprint = 0 print only one line at the last iteration; 0 < iprint < 99 print also f and |proj g| every iprint iterations; iprint = 99 print details of every iteration except n-vectors; iprint = 100 print also the changes of active set and final x; iprint > 100 print details of every iteration including x and g.
max_processes (int | None) – maximum number of processes allowed, has a min. value of 1 if not None.
options (dict | None) – A dictionary of solver options.
max_evals_grouped (int) – Max number of default gradient evaluations performed simultaneously.
kwargs – additional kwargs for scipy.optimize.minimize.

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¶

Methods

get_support_level()¶: Return support level dictionary

static 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(fun, x0, jac=None, bounds=None)[source]¶

Minimize the scalar function.

Parameters:

fun (Callable[[float | ndarray], float]) – The scalar function to minimize.
x0 (float | ndarray) – The initial point for the minimization.
jac (Callable[[float | ndarray], float | ndarray] | None) – The gradient of the scalar function fun.
bounds (list[tuple[float, float]] | None) – Bounds for the variables of fun. This argument might be ignored if the optimizer does not support bounds.

Returns:

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

Return type:

OptimizerResult

print_options()¶: Print algorithm-specific options.

set_max_evals_grouped(limit)¶: Set max evals grouped

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.

static 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