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---
title: ISRES (v0.31)
description: API reference for qiskit.aqua.components.optimizers.ISRES in qiskit v0.31
in_page_toc_min_heading_level: 1
python_api_type: class
python_api_name: qiskit.aqua.components.optimizers.ISRES
---
# ISRES
<Class id="qiskit.aqua.components.optimizers.ISRES" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-aqua/tree/stable/0.9/qiskit/aqua/components/optimizers/nlopts/isres.py" signature="ISRES(max_evals=1000)" modifiers="class">
Bases: `qiskit.aqua.components.optimizers.nlopts.nloptimizer.NLoptOptimizer`
Improved Stochastic Ranking Evolution Strategy optimizer.
Improved Stochastic Ranking Evolution Strategy (ISRES) is an algorithm for non-linearly constrained global optimization. It has heuristics to escape local optima, even though convergence to a global optima is not guaranteed. The evolution strategy is based on a combination of a mutation rule and differential variation. The fitness ranking is simply via the objective function for problems without nonlinear constraints. When nonlinear constraints are included, the [stochastic ranking proposed by Runarsson and Yao](https://www.cs.bham.ac.uk/~xin/papers/published_tec_sep00_constraint.pdf) is employed. This method supports arbitrary nonlinear inequality and equality constraints, in addition to the bound constraints.
NLopt global optimizer, derivative-free. For further detail, please refer to [http://nlopt.readthedocs.io/en/latest/NLopt\_Algorithms/#isres-improved-stochastic-ranking-evolution-strategy](http://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/#isres-improved-stochastic-ranking-evolution-strategy)
**Parameters**
**max\_evals** (`int`) Maximum allowed number of function evaluations.
**Raises**
[**MissingOptionalLibraryError**](qiskit.aqua.MissingOptionalLibraryError "qiskit.aqua.MissingOptionalLibraryError") NLopt library not installed.
## Methods
<span id="qiskit-aqua-components-optimizers-isres-get-nlopt-optimizer" />
### get\_nlopt\_optimizer
<Function id="qiskit.aqua.components.optimizers.ISRES.get_nlopt_optimizer" signature="ISRES.get_nlopt_optimizer()">
Return NLopt optimizer type
**Return type**
`NLoptOptimizerType`
</Function>
<span id="qiskit-aqua-components-optimizers-isres-get-support-level" />
### get\_support\_level
<Function id="qiskit.aqua.components.optimizers.ISRES.get_support_level" signature="ISRES.get_support_level()">
return support level dictionary
</Function>
<span id="qiskit-aqua-components-optimizers-isres-gradient-num-diff" />
### gradient\_num\_diff
<Function id="qiskit.aqua.components.optimizers.ISRES.gradient_num_diff" signature="ISRES.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-isres-optimize" />
### optimize
<Function id="qiskit.aqua.components.optimizers.ISRES.optimize" signature="ISRES.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-isres-print-options" />
### print\_options
<Function id="qiskit.aqua.components.optimizers.ISRES.print_options" signature="ISRES.print_options()">
Print algorithm-specific options.
</Function>
<span id="qiskit-aqua-components-optimizers-isres-set-max-evals-grouped" />
### set\_max\_evals\_grouped
<Function id="qiskit.aqua.components.optimizers.ISRES.set_max_evals_grouped" signature="ISRES.set_max_evals_grouped(limit)">
Set max evals grouped
</Function>
<span id="qiskit-aqua-components-optimizers-isres-set-options" />
### set\_options
<Function id="qiskit.aqua.components.optimizers.ISRES.set_options" signature="ISRES.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-isres-wrap-function" />
### wrap\_function
<Function id="qiskit.aqua.components.optimizers.ISRES.wrap_function" signature="ISRES.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.ISRES.bounds_support_level">
Returns bounds support level
</Attribute>
### gradient\_support\_level
<Attribute id="qiskit.aqua.components.optimizers.ISRES.gradient_support_level">
Returns gradient support level
</Attribute>
### initial\_point\_support\_level
<Attribute id="qiskit.aqua.components.optimizers.ISRES.initial_point_support_level">
Returns initial point support level
</Attribute>
### is\_bounds\_ignored
<Attribute id="qiskit.aqua.components.optimizers.ISRES.is_bounds_ignored">
Returns is bounds ignored
</Attribute>
### is\_bounds\_required
<Attribute id="qiskit.aqua.components.optimizers.ISRES.is_bounds_required">
Returns is bounds required
</Attribute>
### is\_bounds\_supported
<Attribute id="qiskit.aqua.components.optimizers.ISRES.is_bounds_supported">
Returns is bounds supported
</Attribute>
### is\_gradient\_ignored
<Attribute id="qiskit.aqua.components.optimizers.ISRES.is_gradient_ignored">
Returns is gradient ignored
</Attribute>
### is\_gradient\_required
<Attribute id="qiskit.aqua.components.optimizers.ISRES.is_gradient_required">
Returns is gradient required
</Attribute>
### is\_gradient\_supported
<Attribute id="qiskit.aqua.components.optimizers.ISRES.is_gradient_supported">
Returns is gradient supported
</Attribute>
### is\_initial\_point\_ignored
<Attribute id="qiskit.aqua.components.optimizers.ISRES.is_initial_point_ignored">
Returns is initial point ignored
</Attribute>
### is\_initial\_point\_required
<Attribute id="qiskit.aqua.components.optimizers.ISRES.is_initial_point_required">
Returns is initial point required
</Attribute>
### is\_initial\_point\_supported
<Attribute id="qiskit.aqua.components.optimizers.ISRES.is_initial_point_supported">
Returns is initial point supported
</Attribute>
### setting
<Attribute id="qiskit.aqua.components.optimizers.ISRES.setting">
Return setting
</Attribute>
</Class>