# L_BFGS_B#

class 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)[source]#

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 $$f$$. 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 (SupportsFloat) -- 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 (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

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_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

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:

Return type:

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

Minimize the scalar function.

Parameters:
• fun (Callable[[POINT], float]) -- The scalar function to minimize.

• x0 (POINT) -- The initial point for the minimization.

• jac (Callable[[POINT], POINT] | 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