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:
- ——-
- dispbool
- Set to True to print convergence messages. 
- maxiter, maxfevint
- Maximum allowed number of iterations and function evaluations. Will default to - N*200, where- Nis 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 the- N+1vertices in the simplex, where- Nis 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 - Boundsclass.
- 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