217 lines
8.4 KiB
Plaintext
217 lines
8.4 KiB
Plaintext
---
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title: L_BFGS_B
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description: API reference for qiskit.algorithms.optimizers.L_BFGS_B
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in_page_toc_min_heading_level: 1
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python_api_type: class
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python_api_name: qiskit.algorithms.optimizers.L_BFGS_B
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---
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# L\_BFGS\_B
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<Class id="qiskit.algorithms.optimizers.L_BFGS_B" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.22/qiskit/algorithms/optimizers/l_bfgs_b.py" signature="L_BFGS_B(maxfun=15000, maxiter=15000, ftol=2.220446049250313e-15, iprint=- 1, eps=1e-08, options=None, max_evals_grouped=1, **kwargs)" modifiers="class">
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Bases: [`qiskit.algorithms.optimizers.scipy_optimizer.SciPyOptimizer`](qiskit.algorithms.optimizers.SciPyOptimizer "qiskit.algorithms.optimizers.scipy_optimizer.SciPyOptimizer")
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Limited-memory BFGS Bound optimizer.
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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.
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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.
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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.
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Uses `scipy.optimize.fmin_l_bfgs_b`. For further detail, please refer to [https://docs.scipy.org/doc/scipy/reference/optimize.minimize-lbfgsb.html](https://docs.scipy.org/doc/scipy/reference/optimize.minimize-lbfgsb.html)
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**Parameters**
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* **maxfun** (`int`) – Maximum number of function evaluations.
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* **maxiter** (`int`) – Maximum number of iterations.
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* **ftol** (`float`) – The iteration stops when $(f^k - f^{k+1}) / \max\{|f^k|, |f^{k+1}|,1\} \leq \text{ftol}$.
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* **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 $g$.
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* **eps** (`float`) – If jac is approximated, use this value for the step size.
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* **options** (`Optional`\[`dict`]) – A dictionary of solver options.
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* **max\_evals\_grouped** (`int`) – Max number of default gradient evaluations performed simultaneously.
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* **kwargs** – additional kwargs for `scipy.optimize.minimize`.
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## Methods
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### get\_support\_level
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<Function id="qiskit.algorithms.optimizers.L_BFGS_B.get_support_level" signature="L_BFGS_B.get_support_level()">
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Return support level dictionary
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</Function>
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### gradient\_num\_diff
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<Function id="qiskit.algorithms.optimizers.L_BFGS_B.gradient_num_diff" signature="L_BFGS_B.gradient_num_diff(x_center, f, epsilon, max_evals_grouped=None)" modifiers="static">
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We compute the gradient with the numeric differentiation in the parallel way, around the point x\_center.
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**Parameters**
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* **x\_center** (*ndarray*) – point around which we compute the gradient
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* **f** (*func*) – the function of which the gradient is to be computed.
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* **epsilon** (*float*) – the epsilon used in the numeric differentiation.
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* **max\_evals\_grouped** (*int*) – max evals grouped, defaults to 1 (i.e. no batching).
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**Returns**
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the gradient computed
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**Return type**
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grad
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</Function>
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### minimize
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<Function id="qiskit.algorithms.optimizers.L_BFGS_B.minimize" signature="L_BFGS_B.minimize(fun, x0, jac=None, bounds=None)">
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Minimize the scalar function.
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**Parameters**
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* **fun** (`Callable`\[\[`Union`\[`float`, `ndarray`]], `float`]) – The scalar function to minimize.
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* **x0** (`Union`\[`float`, `ndarray`]) – The initial point for the minimization.
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* **jac** (`Optional`\[`Callable`\[\[`Union`\[`float`, `ndarray`]], `Union`\[`float`, `ndarray`]]]) – The gradient of the scalar function `fun`.
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* **bounds** (`Optional`\[`List`\[`Tuple`\[`float`, `float`]]]) – Bounds for the variables of `fun`. This argument might be ignored if the optimizer does not support bounds.
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**Return type**
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[`OptimizerResult`](qiskit.algorithms.optimizers.OptimizerResult "qiskit.algorithms.optimizers.optimizer.OptimizerResult")
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**Returns**
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The result of the optimization, containing e.g. the result as attribute `x`.
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</Function>
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### print\_options
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<Function id="qiskit.algorithms.optimizers.L_BFGS_B.print_options" signature="L_BFGS_B.print_options()">
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Print algorithm-specific options.
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</Function>
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### set\_max\_evals\_grouped
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<Function id="qiskit.algorithms.optimizers.L_BFGS_B.set_max_evals_grouped" signature="L_BFGS_B.set_max_evals_grouped(limit)">
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Set max evals grouped
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</Function>
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### set\_options
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<Function id="qiskit.algorithms.optimizers.L_BFGS_B.set_options" signature="L_BFGS_B.set_options(**kwargs)">
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Sets or updates values in the options dictionary.
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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.
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**Parameters**
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**kwargs** (*dict*) – options, given as name=value.
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</Function>
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### wrap\_function
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<Function id="qiskit.algorithms.optimizers.L_BFGS_B.wrap_function" signature="L_BFGS_B.wrap_function(function, args)" modifiers="static">
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Wrap the function to implicitly inject the args at the call of the function.
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**Parameters**
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* **function** (*func*) – the target function
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* **args** (*tuple*) – the args to be injected
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**Returns**
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wrapper
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**Return type**
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function\_wrapper
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</Function>
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## Attributes
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### bounds\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.bounds_support_level">
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Returns bounds support level
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</Attribute>
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### gradient\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.gradient_support_level">
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Returns gradient support level
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</Attribute>
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### initial\_point\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.initial_point_support_level">
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Returns initial point support level
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</Attribute>
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### is\_bounds\_ignored
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.is_bounds_ignored">
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Returns is bounds ignored
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</Attribute>
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### is\_bounds\_required
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.is_bounds_required">
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Returns is bounds required
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</Attribute>
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### is\_bounds\_supported
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.is_bounds_supported">
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Returns is bounds supported
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</Attribute>
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### is\_gradient\_ignored
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.is_gradient_ignored">
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Returns is gradient ignored
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</Attribute>
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### is\_gradient\_required
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.is_gradient_required">
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Returns is gradient required
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</Attribute>
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### is\_gradient\_supported
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.is_gradient_supported">
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Returns is gradient supported
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</Attribute>
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### is\_initial\_point\_ignored
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.is_initial_point_ignored">
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Returns is initial point ignored
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</Attribute>
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### is\_initial\_point\_required
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.is_initial_point_required">
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Returns is initial point required
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</Attribute>
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### is\_initial\_point\_supported
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.is_initial_point_supported">
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Returns is initial point supported
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</Attribute>
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### setting
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.setting">
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Return setting
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</Attribute>
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### settings
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.settings">
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**Return type**
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`Dict`\[`str`, `Any`]
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</Attribute>
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</Class>
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