209 lines
9.9 KiB
Plaintext
209 lines
9.9 KiB
Plaintext
---
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title: SlsqpOptimizer
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description: API reference for qiskit.optimization.algorithms.SlsqpOptimizer
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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.optimization.algorithms.SlsqpOptimizer
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---
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# qiskit.optimization.algorithms.SlsqpOptimizer
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<Class id="qiskit.optimization.algorithms.SlsqpOptimizer" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-aqua/tree/stable/0.9/qiskit/optimization/algorithms/slsqp_optimizer.py" signature="SlsqpOptimizer(iter=100, acc=1e-06, iprint=0, trials=1, clip=100.0, full_output=False)" modifiers="class">
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The SciPy SLSQP optimizer wrapped as an Qiskit [`OptimizationAlgorithm`](qiskit.optimization.algorithms.OptimizationAlgorithm "qiskit.optimization.algorithms.OptimizationAlgorithm").
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This class provides a wrapper for `scipy.optimize.fmin_slsqp` ([https://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.optimize.fmin\_slsqp.html](https://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.optimize.fmin_slsqp.html)) to be used within the optimization module. The arguments for `fmin_slsqp` are passed via the constructor.
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**Examples**
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```python
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>>> from qiskit.optimization.problems import QuadraticProgram
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>>> from qiskit.optimization.algorithms import SlsqpOptimizer
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>>> problem = QuadraticProgram()
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>>> # specify problem here
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>>> x = problem.continuous_var(name="x")
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>>> y = problem.continuous_var(name="y")
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>>> problem.maximize(linear=[2, 0], quadratic=[[-1, 2], [0, -2]])
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>>> optimizer = SlsqpOptimizer()
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>>> result = optimizer.solve(problem)
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```
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Initializes the SlsqpOptimizer.
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This initializer takes the algorithmic parameters of SLSQP and stores them for later use of `fmin_slsqp` when [`solve()`](#qiskit.optimization.algorithms.SlsqpOptimizer.solve "qiskit.optimization.algorithms.SlsqpOptimizer.solve") is invoked. This optimizer can be applied to find a (local) optimum for problems consisting of only continuous variables.
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**Parameters**
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* **iter** (`int`) – The maximum number of iterations.
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* **acc** (`float`) – Requested accuracy.
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* **iprint** (`int`) –
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The verbosity of fmin\_slsqp :
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* iprint \<= 0 : Silent operation
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* iprint == 1 : Print summary upon completion (default)
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* iprint >= 2 : Print status of each iterate and summary
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* **trials** (`int`) – The number of trials for multi-start method. The first trial is solved with the initial guess of zero. If more than one trial is specified then initial guesses are uniformly drawn from `[lowerbound, upperbound]` with potential clipping.
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* **clip** (`float`) – Clipping parameter for the initial guesses in the multi-start method. If a variable is unbounded then the lower bound and/or upper bound are replaced with the `-clip` or `clip` values correspondingly for the initial guesses.
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* **full\_output** (`bool`) – If `False`, return only the minimizer of func (default). Otherwise, output final objective function and summary information.
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### \_\_init\_\_
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<Function id="qiskit.optimization.algorithms.SlsqpOptimizer.__init__" signature="__init__(iter=100, acc=1e-06, iprint=0, trials=1, clip=100.0, full_output=False)">
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Initializes the SlsqpOptimizer.
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This initializer takes the algorithmic parameters of SLSQP and stores them for later use of `fmin_slsqp` when [`solve()`](#qiskit.optimization.algorithms.SlsqpOptimizer.solve "qiskit.optimization.algorithms.SlsqpOptimizer.solve") is invoked. This optimizer can be applied to find a (local) optimum for problems consisting of only continuous variables.
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**Parameters**
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* **iter** (`int`) – The maximum number of iterations.
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* **acc** (`float`) – Requested accuracy.
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* **iprint** (`int`) –
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The verbosity of fmin\_slsqp :
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* iprint \<= 0 : Silent operation
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* iprint == 1 : Print summary upon completion (default)
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* iprint >= 2 : Print status of each iterate and summary
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* **trials** (`int`) – The number of trials for multi-start method. The first trial is solved with the initial guess of zero. If more than one trial is specified then initial guesses are uniformly drawn from `[lowerbound, upperbound]` with potential clipping.
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* **clip** (`float`) – Clipping parameter for the initial guesses in the multi-start method. If a variable is unbounded then the lower bound and/or upper bound are replaced with the `-clip` or `clip` values correspondingly for the initial guesses.
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* **full\_output** (`bool`) – If `False`, return only the minimizer of func (default). Otherwise, output final objective function and summary information.
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</Function>
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## Methods
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| | |
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| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------- |
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| [`__init__`](#qiskit.optimization.algorithms.SlsqpOptimizer.__init__ "qiskit.optimization.algorithms.SlsqpOptimizer.__init__")(\[iter, acc, iprint, trials, clip, …]) | Initializes the SlsqpOptimizer. |
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| [`get_compatibility_msg`](#qiskit.optimization.algorithms.SlsqpOptimizer.get_compatibility_msg "qiskit.optimization.algorithms.SlsqpOptimizer.get_compatibility_msg")(problem) | Checks whether a given problem can be solved with this optimizer. |
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| [`is_compatible`](#qiskit.optimization.algorithms.SlsqpOptimizer.is_compatible "qiskit.optimization.algorithms.SlsqpOptimizer.is_compatible")(problem) | Checks whether a given problem can be solved with the optimizer implementing this method. |
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| [`multi_start_solve`](#qiskit.optimization.algorithms.SlsqpOptimizer.multi_start_solve "qiskit.optimization.algorithms.SlsqpOptimizer.multi_start_solve")(minimize, problem) | Applies a multi start method given a local optimizer. |
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| [`solve`](#qiskit.optimization.algorithms.SlsqpOptimizer.solve "qiskit.optimization.algorithms.SlsqpOptimizer.solve")(problem) | Tries to solves the given problem using the optimizer. |
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## Attributes
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| | |
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| ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------ |
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| [`clip`](#qiskit.optimization.algorithms.SlsqpOptimizer.clip "qiskit.optimization.algorithms.SlsqpOptimizer.clip") | Returns the clip value for this optimizer. |
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| [`trials`](#qiskit.optimization.algorithms.SlsqpOptimizer.trials "qiskit.optimization.algorithms.SlsqpOptimizer.trials") | Returns the number of trials for this optimizer. |
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### clip
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<Attribute id="qiskit.optimization.algorithms.SlsqpOptimizer.clip">
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Returns the clip value for this optimizer.
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**Return type**
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`float`
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**Returns**
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The clip value.
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</Attribute>
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### get\_compatibility\_msg
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<Function id="qiskit.optimization.algorithms.SlsqpOptimizer.get_compatibility_msg" signature="get_compatibility_msg(problem)">
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Checks whether a given problem can be solved with this optimizer.
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Checks whether the given problem is compatible, i.e., whether the problem contains only continuous variables, and otherwise, returns a message explaining the incompatibility.
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**Parameters**
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**problem** (`QuadraticProgram`) – The optimization problem to check compatibility.
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**Return type**
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`str`
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**Returns**
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Returns a string describing the incompatibility.
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</Function>
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### is\_compatible
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<Function id="qiskit.optimization.algorithms.SlsqpOptimizer.is_compatible" signature="is_compatible(problem)">
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Checks whether a given problem can be solved with the optimizer implementing this method.
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**Parameters**
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**problem** (`QuadraticProgram`) – The optimization problem to check compatibility.
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**Return type**
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`bool`
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**Returns**
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Returns True if the problem is compatible, False otherwise.
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</Function>
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### multi\_start\_solve
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<Function id="qiskit.optimization.algorithms.SlsqpOptimizer.multi_start_solve" signature="multi_start_solve(minimize, problem)">
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Applies a multi start method given a local optimizer.
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**Parameters**
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* **minimize** (`Callable`\[\[`ndarray`], `Tuple`\[`ndarray`, `Any`]]) – A callable object that minimizes the problem specified
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* **problem** (`QuadraticProgram`) – A problem to solve
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**Return type**
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`OptimizationResult`
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**Returns**
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The result of the multi start algorithm applied to the problem.
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</Function>
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### solve
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<Function id="qiskit.optimization.algorithms.SlsqpOptimizer.solve" signature="solve(problem)">
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Tries to solves the given problem using the optimizer.
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Runs the optimizer to try to solve the optimization problem.
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**Parameters**
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**problem** (`QuadraticProgram`) – The problem to be solved.
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**Return type**
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`OptimizationResult`
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**Returns**
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The result of the optimizer applied to the problem.
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**Raises**
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[**QiskitOptimizationError**](qiskit.optimization.QiskitOptimizationError "qiskit.optimization.QiskitOptimizationError") – If the problem is incompatible with the optimizer.
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</Function>
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### trials
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<Attribute id="qiskit.optimization.algorithms.SlsqpOptimizer.trials">
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Returns the number of trials for this optimizer.
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**Return type**
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`int`
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**Returns**
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The number of trials.
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</Attribute>
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</Class>
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