213 lines
9.0 KiB
Plaintext
213 lines
9.0 KiB
Plaintext
---
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title: NELDER_MEAD
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description: API reference for qiskit.algorithms.optimizers.NELDER_MEAD
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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.NELDER_MEAD
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---
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<span id="nelder-mead" />
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# NELDER\_MEAD
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<Class id="qiskit.algorithms.optimizers.NELDER_MEAD" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.25/qiskit/algorithms/optimizers/nelder_mead.py" signature="qiskit.algorithms.optimizers.NELDER_MEAD(maxiter=None, maxfev=1000, disp=False, xatol=0.0001, tol=None, adaptive=False, options=None, **kwargs)" modifiers="class">
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Bases: [`SciPyOptimizer`](qiskit.algorithms.optimizers.SciPyOptimizer "qiskit.algorithms.optimizers.scipy_optimizer.SciPyOptimizer")
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Nelder-Mead optimizer.
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The Nelder-Mead algorithm performs unconstrained optimization; it ignores bounds or constraints. It is used to find the minimum or maximum of an objective function in a multidimensional space. It is based on the Simplex algorithm. Nelder-Mead is robust in many applications, especially when the first and second derivatives of the objective function are not known.
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However, if the numerical computation of the derivatives can be trusted to be accurate, other algorithms using the first and/or second derivatives information might be preferred to Nelder-Mead for their better performance in the general case, especially in consideration of the fact that the Nelder–Mead technique is a heuristic search method that can converge to non-stationary points.
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Uses scipy.optimize.minimize Nelder-Mead. For further detail, please refer to See [https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html)
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**Parameters**
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* **maxiter** ([*int*](https://docs.python.org/3/library/functions.html#int "(in Python v3.12)") *| None*) – Maximum allowed number of iterations. If both maxiter and maxfev are set, minimization will stop at the first reached.
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* **maxfev** ([*int*](https://docs.python.org/3/library/functions.html#int "(in Python v3.12)")) – Maximum allowed number of function evaluations. If both maxiter and maxfev are set, minimization will stop at the first reached.
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* **disp** ([*bool*](https://docs.python.org/3/library/functions.html#bool "(in Python v3.12)")) – Set to True to print convergence messages.
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* **xatol** ([*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")) – Absolute error in xopt between iterations that is acceptable for convergence.
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* **tol** ([*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)") *| None*) – Tolerance for termination.
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* **adaptive** ([*bool*](https://docs.python.org/3/library/functions.html#bool "(in Python v3.12)")) – Adapt algorithm parameters to dimensionality of problem.
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* **options** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict "(in Python v3.12)") *| None*) – A dictionary of solver options.
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* **kwargs** – additional kwargs for scipy.optimize.minimize.
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## Attributes
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### bounds\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.NELDER_MEAD.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.NELDER_MEAD.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.NELDER_MEAD.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.NELDER_MEAD.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.NELDER_MEAD.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.NELDER_MEAD.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.NELDER_MEAD.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.NELDER_MEAD.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.NELDER_MEAD.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.NELDER_MEAD.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.NELDER_MEAD.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.NELDER_MEAD.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.NELDER_MEAD.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.NELDER_MEAD.settings" />
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## Methods
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### get\_support\_level
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<Function id="qiskit.algorithms.optimizers.NELDER_MEAD.get_support_level" signature="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.NELDER_MEAD.gradient_num_diff" signature="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*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")) – the epsilon used in the numeric differentiation.
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* **max\_evals\_grouped** ([*int*](https://docs.python.org/3/library/functions.html#int "(in Python v3.12)")) – 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.NELDER_MEAD.minimize" signature="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\[\[POINT],* [*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")*]*) – The scalar function to minimize.
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* **x0** (*POINT*) – The initial point for the minimization.
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* **jac** (*Callable\[\[POINT], POINT] | None*) – The gradient of the scalar function `fun`.
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* **bounds** ([*list*](https://docs.python.org/3/library/stdtypes.html#list "(in Python v3.12)")*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple "(in Python v3.12)")*\[*[*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")*,* [*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")*]] | None*) – Bounds for the variables of `fun`. This argument might be ignored if the optimizer does not support bounds.
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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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**Return type**
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[OptimizerResult](qiskit.algorithms.optimizers.OptimizerResult "qiskit.algorithms.optimizers.OptimizerResult")
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</Function>
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### print\_options
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<Function id="qiskit.algorithms.optimizers.NELDER_MEAD.print_options" signature="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.NELDER_MEAD.set_max_evals_grouped" signature="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.NELDER_MEAD.set_options" signature="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*](https://docs.python.org/3/library/stdtypes.html#dict "(in Python v3.12)")) – 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.NELDER_MEAD.wrap_function" signature="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*](https://docs.python.org/3/library/stdtypes.html#tuple "(in Python v3.12)")) – 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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</Class>
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