Bases: `SciPyOptimizer`

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.

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.

Parameters:
• maxiter (int | None) – Maximum allowed number of iterations. If both maxiter and maxfev are set, minimization will stop at the first reached.

• maxfev (int) – Maximum allowed number of function evaluations. If both maxiter and maxfev are set, minimization will stop at the first reached.

• disp (bool) – Set to True to print convergence messages.

• xatol (float) – Absolute error in xopt between iterations that is acceptable for convergence.

• tol (float | None) – Tolerance for termination.

• options (dict | None) – A dictionary of solver options.

• kwargs – additional kwargs for scipy.optimize.minimize.

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

setting#

Return setting

settings#

Methods

get_support_level()#

Return support level dictionary

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, defaults to 1 (i.e. no batching).

Returns:

Return type:

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

Minimize the scalar function.

Parameters:
• fun (Callable[[POINT], 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[tuple[float, 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

print_options()#

Print algorithm-specific options.

set_max_evals_grouped(limit)#

Set max evals grouped

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.

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) – the args to be injected

Returns:

wrapper

Return type:

function_wrapper