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# minimize(method=’Nelder-Mead’)

scipy.optimize.minimize(fun, x0, args=(), method=None, jac=None, hess=None, hessp=None, bounds=None, constraints=(), tol=None, callback=None, options=None)

  Minimization of scalar function of one or more variables using the Nelder-Mead algorithm.

  See also

  For documentation for the rest of the parameters, see `scipy.optimize.minimize`

  Options:
    ——-
    disp bool

      Set to True to print convergence messages.

    maxiter, maxfev int

      Maximum allowed number of iterations and function evaluations. Will default to `N*200`, where `N` is the number of variables, if neither maxiter or maxfev is set. If both maxiter and maxfev are set, minimization will stop at the first reached.

    return_all bool, optional

      Set to True to return a list of the best solution at each of the iterations.

    initial_simplex array_like of shape (N + 1, N)

      Initial simplex. If given, overrides x0. `initial_simplex[j,:]` should contain the coordinates of the jth vertex of the `N+1` vertices in the simplex, where `N` is the dimension.

    xatol float, optional

      Absolute error in xopt between iterations that is acceptable for convergence.

    fatol number, optional

      Absolute error in func(xopt) between iterations that is acceptable for convergence.

    adaptive bool, optional

      Adapt algorithm parameters to dimensionality of problem. Useful for high-dimensional minimization [1].

    bounds sequence or `Bounds`, optional

      Bounds on variables. There are two ways to specify the bounds:

      1. Instance of `Bounds` class.

      2. Sequence of `(min, max)` pairs for each element in x. None is used to specify no bound.

      Note that this just clips all vertices in simplex based on the bounds.

  References

  [1]

  Gao, F. and Han, L. Implementing the Nelder-Mead simplex algorithm with adaptive parameters. 2012. Computational Optimization and Applications. 51:1, pp. 259-277