# NFT

`qiskit.algorithms.optimizers.NFT(maxiter=None, maxfev=1024, disp=False, reset_interval=32, options=None, **kwargs)`

Bases: `SciPyOptimizer`

Nakanishi-Fujii-Todo algorithm.

Built out using scipy framework, for details, please refer to https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html (opens in a new tab).

Parameters

## Notes

In this optimization method, the optimization function have to satisfy three conditions written in [1].

## References

[1]

K. M. Nakanishi, K. Fujii, and S. Todo. 2019. Sequential minimal optimization for quantum-classical hybrid algorithms. arXiv preprint arXiv:1903.12166.

## Attributes

### bounds_support_level

Returns bounds 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_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

Return setting

## Methods

### get_support_level

`get_support_level()`

Return support level dictionary

`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 (opens in a new tab)) – the epsilon used in the numeric differentiation.
• max_evals_grouped (int (opens in a new tab)) – max evals grouped, defaults to 1 (i.e. no batching).

Returns

Return type

### minimize

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

Minimize the scalar function.

Parameters

Returns

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

Return type

OptimizerResult

`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 (opens in a new tab)) – 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

Returns

wrapper

Return type

function_wrapper