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---
title: L_BFGS_B (v0.31)
description: API reference for qiskit.aqua.components.optimizers.L_BFGS_B in qiskit v0.31
in_page_toc_min_heading_level: 1
python_api_type: class
python_api_name: qiskit.aqua.components.optimizers.L_BFGS_B
---
<span id="l-bfgs-b" />
# L\_BFGS\_B
<Class id="qiskit.aqua.components.optimizers.L_BFGS_B" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-aqua/tree/stable/0.9/qiskit/aqua/components/optimizers/l_bfgs_b.py" signature="L_BFGS_B(maxfun=1000, maxiter=15000, factr=10, iprint=- 1, epsilon=1e-08)" modifiers="class">
Bases: `qiskit.aqua.components.optimizers.optimizer.Optimizer`
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 Newtonss 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/generated/scipy.optimize.fmin\_l\_bfgs\_b.html](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin_l_bfgs_b.html)
**Parameters**
* **maxfun** (`int`) Maximum number of function evaluations.
* **maxiter** (`int`) Maximum number of iterations.
* **factr** (`float`) The iteration stops 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.
* **epsilon** (`float`) Step size used when approx\_grad is True, for numerically calculating the gradient
## Methods
<span id="qiskit-aqua-components-optimizers-l-bfgs-b-get-support-level" />
### get\_support\_level
<Function id="qiskit.aqua.components.optimizers.L_BFGS_B.get_support_level" signature="L_BFGS_B.get_support_level()">
Return support level dictionary
</Function>
<span id="qiskit-aqua-components-optimizers-l-bfgs-b-gradient-num-diff" />
### gradient\_num\_diff
<Function id="qiskit.aqua.components.optimizers.L_BFGS_B.gradient_num_diff" signature="L_BFGS_B.gradient_num_diff(x_center, f, epsilon, max_evals_grouped=1)" modifiers="static">
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
</Function>
<span id="qiskit-aqua-components-optimizers-l-bfgs-b-optimize" />
### optimize
<Function id="qiskit.aqua.components.optimizers.L_BFGS_B.optimize" signature="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
</Function>
<span id="qiskit-aqua-components-optimizers-l-bfgs-b-print-options" />
### print\_options
<Function id="qiskit.aqua.components.optimizers.L_BFGS_B.print_options" signature="L_BFGS_B.print_options()">
Print algorithm-specific options.
</Function>
<span id="qiskit-aqua-components-optimizers-l-bfgs-b-set-max-evals-grouped" />
### set\_max\_evals\_grouped
<Function id="qiskit.aqua.components.optimizers.L_BFGS_B.set_max_evals_grouped" signature="L_BFGS_B.set_max_evals_grouped(limit)">
Set max evals grouped
</Function>
<span id="qiskit-aqua-components-optimizers-l-bfgs-b-set-options" />
### set\_options
<Function id="qiskit.aqua.components.optimizers.L_BFGS_B.set_options" signature="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.
</Function>
<span id="qiskit-aqua-components-optimizers-l-bfgs-b-wrap-function" />
### wrap\_function
<Function id="qiskit.aqua.components.optimizers.L_BFGS_B.wrap_function" signature="L_BFGS_B.wrap_function(function, args)" modifiers="static">
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
</Function>
## Attributes
### bounds\_support\_level
<Attribute id="qiskit.aqua.components.optimizers.L_BFGS_B.bounds_support_level">
Returns bounds support level
</Attribute>
### gradient\_support\_level
<Attribute id="qiskit.aqua.components.optimizers.L_BFGS_B.gradient_support_level">
Returns gradient support level
</Attribute>
### initial\_point\_support\_level
<Attribute id="qiskit.aqua.components.optimizers.L_BFGS_B.initial_point_support_level">
Returns initial point support level
</Attribute>
### is\_bounds\_ignored
<Attribute id="qiskit.aqua.components.optimizers.L_BFGS_B.is_bounds_ignored">
Returns is bounds ignored
</Attribute>
### is\_bounds\_required
<Attribute id="qiskit.aqua.components.optimizers.L_BFGS_B.is_bounds_required">
Returns is bounds required
</Attribute>
### is\_bounds\_supported
<Attribute id="qiskit.aqua.components.optimizers.L_BFGS_B.is_bounds_supported">
Returns is bounds supported
</Attribute>
### is\_gradient\_ignored
<Attribute id="qiskit.aqua.components.optimizers.L_BFGS_B.is_gradient_ignored">
Returns is gradient ignored
</Attribute>
### is\_gradient\_required
<Attribute id="qiskit.aqua.components.optimizers.L_BFGS_B.is_gradient_required">
Returns is gradient required
</Attribute>
### is\_gradient\_supported
<Attribute id="qiskit.aqua.components.optimizers.L_BFGS_B.is_gradient_supported">
Returns is gradient supported
</Attribute>
### is\_initial\_point\_ignored
<Attribute id="qiskit.aqua.components.optimizers.L_BFGS_B.is_initial_point_ignored">
Returns is initial point ignored
</Attribute>
### is\_initial\_point\_required
<Attribute id="qiskit.aqua.components.optimizers.L_BFGS_B.is_initial_point_required">
Returns is initial point required
</Attribute>
### is\_initial\_point\_supported
<Attribute id="qiskit.aqua.components.optimizers.L_BFGS_B.is_initial_point_supported">
Returns is initial point supported
</Attribute>
### setting
<Attribute id="qiskit.aqua.components.optimizers.L_BFGS_B.setting">
Return setting
</Attribute>
</Class>