Skip to main contentIBM Quantum Documentation
You are viewing the API reference for an old version of Qiskit SDK. Switch to latest version

L_BFGS_B

qiskit.algorithms.optimizers.L_BFGS_B(maxfun=15000, maxiter=15000, ftol=2.220446049250313e-15, iprint=-1, eps=1e-08, options=None, max_evals_grouped=1, **kwargs) GitHub(opens in a new tab)

Bases: 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 ff. This optimizer is a quasi-Newton method, meaning that, in contrast to Newtons’s method, it does not require ff’s Hessian (the matrix of ff’s second derivatives) when attempting to compute ff’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 ff are used to identify the direction of steepest descent, and also to form an estimate of the Hessian matrix (second derivative) of ff. 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(opens in a new tab)

Parameters

  • maxfun (int(opens in a new tab)) – Maximum number of function evaluations.
  • maxiter (int(opens in a new tab)) – Maximum number of iterations.
  • ftol (SupportsFloat) – The iteration stops when (fkfk+1)/max{fk,fk+1,1}ftol(f^k - f^{k+1}) / \max\{|f^k|, |f^{k+1}|,1\} \leq \text{ftol}.
  • iprint (int(opens in a new tab)) – 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 ff and projg|\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 xx; iprint > 100 print details of every iteration including xx and gg.
  • eps (float(opens in a new tab)) – If jac is approximated, use this value for the step size.
  • options (dict(opens in a new tab) | None) – A dictionary of solver options.
  • max_evals_grouped (int(opens in a new tab)) – 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

get_support_level()

Return support level dictionary

gradient_num_diff

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(opens in a new tab)) – the epsilon used in the numeric differentiation.
  • max_evals_grouped (int(opens in a new tab)) – max evals grouped, defaults to 1 (i.e. no batching).

Returns

the gradient computed

Return type

grad

minimize

minimize(fun, x0, jac=None, bounds=None)

Minimize the scalar function.

Parameters

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

set_max_evals_grouped(limit)

Set max evals grouped

set_options

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(opens in a new tab)) – options, given as name=value.

wrap_function

static wrap_function(function, args)

Wrap the function to implicitly inject the args at the call of the function.

Parameters

Returns

wrapper

Return type

function_wrapper

Was this page helpful?
Report a bug or request content on GitHub.