P_BFGS¶

class P_BFGS(maxfun=1000, factr=10, iprint=- 1, max_processes=None)[source]¶

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.
factr (float) – 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.

Attributes

`P_BFGS.bounds_support_level`	Returns bounds support level
`P_BFGS.gradient_support_level`	Returns gradient support level
`P_BFGS.initial_point_support_level`	Returns initial point support level
`P_BFGS.is_bounds_ignored`	Returns is bounds ignored
`P_BFGS.is_bounds_required`	Returns is bounds required
`P_BFGS.is_bounds_supported`	Returns is bounds supported
`P_BFGS.is_gradient_ignored`	Returns is gradient ignored
`P_BFGS.is_gradient_required`	Returns is gradient required
`P_BFGS.is_gradient_supported`	Returns is gradient supported
`P_BFGS.is_initial_point_ignored`	Returns is initial point ignored
`P_BFGS.is_initial_point_required`	Returns is initial point required
`P_BFGS.is_initial_point_supported`	Returns is initial point supported
`P_BFGS.setting`	Return setting

Methods

`P_BFGS.get_support_level`()	return support level dictionary
`P_BFGS.gradient_num_diff`(x_center, f, epsilon)	We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
`P_BFGS.optimize`(num_vars, objective_function)	Perform optimization.
`P_BFGS.print_options`()	Print algorithm-specific options.
`P_BFGS.set_max_evals_grouped`(limit)	Set max evals grouped
`P_BFGS.set_options`(**kwargs)	Sets or updates values in the options dictionary.
`P_BFGS.wrap_function`(function, args)	Wrap the function to implicitly inject the args at the call of the function.