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L_BFGS_B

class L_BFGS_B(maxfun=15000, maxiter=15000, ftol=2.220446049250313e-15, iprint=- 1, eps=1e-08, options=None, max_evals_grouped=1, **kwargs)[source]

Bases: qiskit.algorithms.optimizers.scipy_optimizer.SciPyOptimizer

Limited-memory BFGS Bound optimizer.

The target goal of Limited-memory Broyden-Fletcher-Goldfarb-Shanno Bound (L-BFGS-B) is to minimize the value of a differentiable scalar function \(f\). This optimizer is a quasi-Newton method, meaning that, in contrast to Newtons’s method, it does not require \(f\)’s Hessian (the matrix of \(f\)’s second derivatives) when attempting to compute \(f\)’s minimum value.

Like BFGS, L-BFGS is an iterative method for solving unconstrained, non-linear optimization problems, but approximates BFGS using a limited amount of computer memory. L-BFGS starts with an initial estimate of the optimal value, and proceeds iteratively to refine that estimate with a sequence of better estimates.

The derivatives of \(f\) are used to identify the direction of steepest descent, and also to form an estimate of the Hessian matrix (second derivative) of \(f\). L-BFGS-B extends L-BFGS to handle simple, per-variable bound constraints.

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

Parameters
  • maxfun (int) – Maximum number of function evaluations.

  • maxiter (int) – Maximum number of iterations.

  • ftol (float) – The iteration stops when \((f^k - f^{k+1}) / \max\{|f^k|, |f^{k+1}|,1\} \leq \text{ftol}\).

  • 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 \(|\text{proj} g|\) 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 \(g\).

  • eps (float) – If jac is approximated, use this value for the step size.

  • 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.

minimize

Minimize the scalar function.

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]