_minimize_neldermead(func, x0, args=(), callback=None, maxiter=None, maxfev=None, disp=False, return_all=False, initial_simplex=None, xatol=0.0001, fatol=0.0001, adaptive=False, bounds=None, **unknown_options)
Set to True to print convergence messages.
Maximum allowed number of iterations and function evaluations. Will default to N*200
, where N
is the number of variables, if neither :None:None:`maxiter` or :None:None:`maxfev` is set. If both :None:None:`maxiter` and :None:None:`maxfev` are set, minimization will stop at the first reached.
Set to True to return a list of the best solution at each of the iterations.
Initial simplex. If given, overrides :None:None:`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.
Absolute error in xopt between iterations that is acceptable for convergence.
Absolute error in func(xopt) between iterations that is acceptable for convergence.
Adapt algorithm parameters to dimensionality of problem. Useful for high-dimensional minimization .
Bounds on variables. There are two ways to specify the bounds:
Note that this just clips all vertices in simplex based on the bounds.
Minimization of scalar function of one or more variables using the Nelder-Mead algorithm.
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