P_BFGS¶

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

Bases: qiskit.algorithms.optimizers.scipy_optimizer.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

Parameters

maxfun (int) – Maximum number of function evaluations.
ftol (float) – The iteration stops when (f^k - f^{k+1})/max{|f^k|,|f^{k+1}|,1} <= ftol.
factr (Optional[float]) – (DEPRECATED) The iteration stops when (f^k - f^{k+1})/max{|f^k|, |f^{k+1}|,1} <= factr * eps, where eps is the machine precision, which is automatically generated by the code. Typical values for factr are: 1e12 for low accuracy; 1e7 for moderate accuracy; 10.0 for extremely high accuracy. See Notes for relationship to ftol, which is exposed (instead of factr) by the scipy.optimize.minimize interface to L-BFGS-B.
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 (Optional[int]) – maximum number of processes allowed, has a min. value of 1 if not None.
options (Optional[dict]) – A dictionary of solver options.
max_evals_grouped (int) – Max number of default gradient evaluations performed simultaneously.
kwargs – additional kwargs for scipy.optimize.minimize.

Methods

`get_support_level`	Return support level dictionary
`gradient_num_diff`	We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
`optimize`	Perform optimization.
`print_options`	Print algorithm-specific options.
`set_max_evals_grouped`	Set max evals grouped
`set_options`	Sets or updates values in the options dictionary.
`wrap_function`	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¶

Return type: Dict[str, Any]