minimize(method=’Nelder-Mead’)¶
-
scipy.optimize.
minimize
(fun, x0, args=(), method='Nelder-Mead', tol=None, callback=None, options={'func': None, 'maxiter': None, 'maxfev': None, 'disp': False, 'return_all': False, 'initial_simplex': None, 'xatol': 0.0001, 'fatol': 0.0001, 'adaptive': False}) 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
- dispbool
Set to True to print convergence messages.
- maxiter, maxfevint
Maximum allowed number of iterations and function evaluations. Will default to
N*200
, whereN
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.- initial_simplexarray_like of shape (N + 1, N)
Initial simplex. If given, overrides x0.
initial_simplex[j,:]
should contain the coordinates of the j-th vertex of theN+1
vertices in the simplex, whereN
is the dimension.- xatolfloat, optional
Absolute error in xopt between iterations that is acceptable for convergence.
- fatolnumber, optional
Absolute error in func(xopt) between iterations that is acceptable for convergence.
- adaptivebool, optional
Adapt algorithm parameters to dimensionality of problem. Useful for high-dimensional minimization [1].
References