minimize(method=’Nelder-Mead’)#
- scipy.optimize.minimize(fun, x0, args=(), method='Nelder-Mead', bounds=None, 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.- return_allbool, optional
Set to True to return a list of the best solution at each of the iterations.
- initial_simplexarray_like of shape (N + 1, N)
Initial simplex. If given, overrides x0.
initial_simplex[j,:]
should contain the coordinates of the jth 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].
- boundssequence or
Bounds
, optional Bounds on variables. There are two ways to specify the bounds:
Instance of
Bounds
class.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