L_BFGS_B
L_BFGS_B(maxfun=1000, maxiter=15000, ftol=2.220446049250313e-15, factr=None, iprint=- 1, epsilon=1e-08, eps=1e-08, options=None, max_evals_grouped=1, **kwargs)
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 . This optimizer is a quasi-Newton method, meaning that, in contrast to Newtons’s method, it does not require ’s Hessian (the matrix of ’s second derivatives) when attempting to compute ’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 are used to identify the direction of steepest descent, and also to form an estimate of the Hessian matrix (second derivative) of . 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) – 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} <= ftol. - factr (
Optional[float]) – (DEPRECATED) The iteration steps 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. - eps (
float) – If jac is approximated, use this value for the step size. - epsilon (
float) – (DEPRECATED) Step size used when approx_grad is True, for numerically calculating the gradient - 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
L_BFGS_B.get_support_level()
Return support level dictionary
gradient_num_diff
static L_BFGS_B.gradient_num_diff(x_center, f, epsilon, max_evals_grouped=1)
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
Returns
the gradient computed
Return type
grad
minimize
L_BFGS_B.minimize(fun, x0, jac=None, bounds=None)
Minimize the scalar function.
Parameters
- fun (
Callable[[Union[float,ndarray]],float]) – The scalar function to minimize. - x0 (
Union[float,ndarray]) – The initial point for the minimization. - jac (
Optional[Callable[[Union[float,ndarray]],Union[float,ndarray]]]) – The gradient of the scalar functionfun. - bounds (
Optional[List[Tuple[float,float]]]) – Bounds for the variables offun. This argument might be ignored if the optimizer does not support bounds.
Return type
OptimizerResult
Returns
The result of the optimization, containing e.g. the result as attribute x.
optimize
L_BFGS_B.optimize(num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None)
Perform optimization.
Parameters
- num_vars (int) – Number of parameters to be optimized.
- objective_function (callable) – A function that computes the objective function.
- gradient_function (callable) – A function that computes the gradient of the objective function, or None if not available.
- variable_bounds (list[(float, float)]) – List of variable bounds, given as pairs (lower, upper). None means unbounded.
- initial_point (numpy.ndarray[float]) – Initial point.
Returns
point, value, nfev
point: is a 1D numpy.ndarray[float] containing the solution value: is a float with the objective function value nfev: number of objective function calls made if available or None
Raises
ValueError – invalid input
print_options
L_BFGS_B.print_options()
Print algorithm-specific options.
set_max_evals_grouped
L_BFGS_B.set_max_evals_grouped(limit)
Set max evals grouped
set_options
L_BFGS_B.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.
wrap_function
static L_BFGS_B.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
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]