---
title: SLSQP (v0.46)
description: API reference for qiskit.algorithms.optimizers.SLSQP in qiskit v0.46
source: https://eu-de.quantum.cloud.ibm.com/docs/en/api/qiskit/0.46/qiskit.algorithms.optimizers.SLSQP
---

# SLSQP

*class* `qiskit.algorithms.optimizers.SLSQP(maxiter=100, disp=False, ftol=1e-06, tol=None, eps=1.4901161193847656e-08, options=None, max_evals_grouped=1, **kwargs)`

[GitHub](https://github.com/qiskit/qiskit/tree/stable/0.46/qiskit/algorithms/optimizers/slsqp.py)

Bases: [`SciPyOptimizer`](/docs/api/qiskit/0.46/qiskit.algorithms.optimizers.SciPyOptimizer "qiskit.algorithms.optimizers.scipy_optimizer.SciPyOptimizer")

Sequential Least SQuares Programming optimizer.

SLSQP minimizes a function of several variables with any combination of bounds, equality and inequality constraints. The method wraps the SLSQP Optimization subroutine originally implemented by Dieter Kraft.

SLSQP is ideal for mathematical problems for which the objective function and the constraints are twice continuously differentiable. Note that the wrapper handles infinite values in bounds by converting them into large floating values.

Uses scipy.optimize.minimize SLSQP. 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)

**Parameters**

- **maxiter** ([*int*](https://docs.python.org/3/library/functions.html#int)) – Maximum number of iterations.
- **disp** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – Set to True to print convergence messages.
- **ftol** ([*float*](https://docs.python.org/3/library/functions.html#float)) – Precision goal for the value of f in the stopping criterion.
- **tol** ([*float*](https://docs.python.org/3/library/functions.html#float) *| None*) – Tolerance for termination.
- **eps** ([*float*](https://docs.python.org/3/library/functions.html#float)) – Step size used for numerical approximation of the Jacobian.
- **options** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict) *| None*) – A dictionary of solver options.
- **max\_evals\_grouped** ([*int*](https://docs.python.org/3/library/functions.html#int)) – Max number of default gradient evaluations performed simultaneously.
- **kwargs** – additional kwargs for scipy.optimize.minimize.

## Attributes

### bounds\_support\_level

Returns bounds support level

### gradient\_support\_level

Returns gradient support level

### initial\_point\_support\_level

Returns initial point support level

### is\_bounds\_ignored

Returns is bounds ignored

### is\_bounds\_required

Returns is bounds required

### is\_bounds\_supported

Returns is bounds supported

### is\_gradient\_ignored

Returns is gradient ignored

### is\_gradient\_required

Returns is gradient required

### is\_gradient\_supported

Returns is gradient supported

### is\_initial\_point\_ignored

Returns is initial point ignored

### is\_initial\_point\_required

Returns is initial point required

### is\_initial\_point\_supported

Returns is initial point supported

### setting

Return setting

### settings

## Methods

### get\_support\_level

`get_support_level()`

Return support level dictionary

### gradient\_num\_diff

*static* `gradient_num_diff(x_center, f, epsilon, max_evals_grouped=None)`

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*](https://docs.python.org/3/library/functions.html#float)) – the epsilon used in the numeric differentiation.
- **max\_evals\_grouped** ([*int*](https://docs.python.org/3/library/functions.html#int)) – max evals grouped, defaults to 1 (i.e. no batching).

**Returns**

the gradient computed

**Return type**

grad

### minimize

`minimize(fun, x0, jac=None, bounds=None)`

Minimize the scalar function.

**Parameters**

- **fun** (*Callable\[\[POINT],* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The scalar function to minimize.
- **x0** (*POINT*) – The initial point for the minimization.
- **jac** (*Callable\[\[POINT], POINT] | None*) – The gradient of the scalar function `fun`.
- **bounds** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]] | None*) – Bounds for the variables of `fun`. This argument might be ignored if the optimizer does not support bounds.

**Returns**

The result of the optimization, containing e.g. the result as attribute `x`.

**Return type**

[OptimizerResult](/docs/api/qiskit/0.46/qiskit.algorithms.optimizers.OptimizerResult "qiskit.algorithms.optimizers.OptimizerResult")

### print\_options

`print_options()`

Print algorithm-specific options.

### set\_max\_evals\_grouped

`set_max_evals_grouped(limit)`

Set max evals grouped

### set\_options

`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*](https://docs.python.org/3/library/stdtypes.html#dict)) – options, given as name=value.

### wrap\_function

*static* `wrap_function(function, args)`

Wrap the function to implicitly inject the args at the call of the function.

**Parameters**

- **function** (*func*) – the target function
- **args** ([*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)) – the args to be injected

**Returns**

wrapper

**Return type**

function\_wrapper