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)
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.
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)
- 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 .
- iprint (int (opens in a new tab)) – Controls the frequency of output.
iprint < 0means no output;
iprint = 0print only one line at the last iteration;
0 < iprint < 99print also and every iprint iterations;
iprint = 99print details of every iteration except n-vectors;
iprint = 100print also the changes of active set and final ;
iprint > 100print details of every iteration including and .
- 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
Returns bounds support level
Returns gradient support level
Returns initial point support level
Returns is bounds ignored
Returns is bounds required
Returns is bounds supported
Returns is gradient ignored
Returns is gradient required
Returns is gradient supported
Returns is initial point ignored
Returns is initial point required
Returns is initial point supported
Return support level dictionary
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.
- 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).
the gradient computed
minimize(fun, x0, jac=None, bounds=None)
Minimize the scalar function.
- fun (Callable[[POINT], float (opens in a new tab)]) – The scalar function to minimize.
- x0 (POINT) – The initial point for the minimization.
- jac (Callable[[POINT], POINT] | None) – The gradient of the scalar function
- bounds (list (opens in a new tab)[tuple (opens in a new tab)[float (opens in a new tab), float (opens in a new tab)]] | None) – Bounds for the variables of
fun. This argument might be ignored if the optimizer does not support bounds.
The result of the optimization, containing e.g. the result as attribute
Print algorithm-specific options.
Set max evals grouped
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.
kwargs (dict (opens in a new tab)) – options, given as name=value.
static wrap_function(function, args)
Wrap the function to implicitly inject the args at the call of the function.
- function (func) – the target function
- args (tuple (opens in a new tab)) – the args to be injected