224 lines
8.4 KiB
Plaintext
224 lines
8.4 KiB
Plaintext
---
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title: NELDER_MEAD (v0.31)
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description: API reference for qiskit.aqua.components.optimizers.NELDER_MEAD in qiskit v0.31
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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.aqua.components.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.aqua.components.optimizers.NELDER_MEAD" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-aqua/tree/stable/0.9/qiskit/aqua/components/optimizers/nelder_mead.py" signature="NELDER_MEAD(maxiter=None, maxfev=1000, disp=False, xatol=0.0001, tol=None, adaptive=False)" modifiers="class">
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Bases: `qiskit.aqua.components.optimizers.optimizer.Optimizer`
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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** (`Optional`\[`int`]) – 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`) – 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`) – Set to True to print convergence messages.
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* **xatol** (`float`) – Absolute error in xopt between iterations that is acceptable for convergence.
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* **tol** (`Optional`\[`float`]) – Tolerance for termination.
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* **adaptive** (`bool`) – Adapt algorithm parameters to dimensionality of problem.
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## Methods
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<span id="qiskit-aqua-components-optimizers-nelder-mead-get-support-level" />
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### get\_support\_level
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<Function id="qiskit.aqua.components.optimizers.NELDER_MEAD.get_support_level" signature="NELDER_MEAD.get_support_level()">
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Return support level dictionary
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</Function>
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<span id="qiskit-aqua-components-optimizers-nelder-mead-gradient-num-diff" />
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### gradient\_num\_diff
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<Function id="qiskit.aqua.components.optimizers.NELDER_MEAD.gradient_num_diff" signature="NELDER_MEAD.gradient_num_diff(x_center, f, epsilon, max_evals_grouped=1)" 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
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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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<span id="qiskit-aqua-components-optimizers-nelder-mead-optimize" />
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### optimize
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<Function id="qiskit.aqua.components.optimizers.NELDER_MEAD.optimize" signature="NELDER_MEAD.optimize(num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None)">
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Perform optimization.
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**Parameters**
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* **num\_vars** (*int*) – Number of parameters to be optimized.
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* **objective\_function** (*callable*) – A function that computes the objective function.
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* **gradient\_function** (*callable*) – A function that computes the gradient of the objective function, or None if not available.
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* **variable\_bounds** (*list\[(float, float)]*) – List of variable bounds, given as pairs (lower, upper). None means unbounded.
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* **initial\_point** (*numpy.ndarray\[float]*) – Initial point.
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**Returns**
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**point, value, nfev**
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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
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**Raises**
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**ValueError** – invalid input
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</Function>
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<span id="qiskit-aqua-components-optimizers-nelder-mead-print-options" />
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### print\_options
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<Function id="qiskit.aqua.components.optimizers.NELDER_MEAD.print_options" signature="NELDER_MEAD.print_options()">
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Print algorithm-specific options.
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</Function>
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<span id="qiskit-aqua-components-optimizers-nelder-mead-set-max-evals-grouped" />
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### set\_max\_evals\_grouped
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<Function id="qiskit.aqua.components.optimizers.NELDER_MEAD.set_max_evals_grouped" signature="NELDER_MEAD.set_max_evals_grouped(limit)">
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Set max evals grouped
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</Function>
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<span id="qiskit-aqua-components-optimizers-nelder-mead-set-options" />
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### set\_options
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<Function id="qiskit.aqua.components.optimizers.NELDER_MEAD.set_options" signature="NELDER_MEAD.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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<span id="qiskit-aqua-components-optimizers-nelder-mead-wrap-function" />
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### wrap\_function
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<Function id="qiskit.aqua.components.optimizers.NELDER_MEAD.wrap_function" signature="NELDER_MEAD.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.aqua.components.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.aqua.components.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.aqua.components.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.aqua.components.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.aqua.components.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.aqua.components.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.aqua.components.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.aqua.components.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.aqua.components.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.aqua.components.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.aqua.components.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.aqua.components.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.aqua.components.optimizers.NELDER_MEAD.setting">
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Return setting
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</Attribute>
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</Class>
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