# minimize(method=’Powell’)

scipy.optimize.minimize(fun, x0, args=(), method='Powell', bounds=None, tol=None, callback=None, options={'func': None, 'xtol': 0.0001, 'ftol': 0.0001, 'maxiter': None, 'maxfev': None, 'disp': False, 'direc': None, 'return_all': False})

  Minimization of scalar function of one or more variables using the modified Powell 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.

    xtol float

      Relative error in solution xopt acceptable for convergence.

    ftol float

      Relative error in `fun(xopt)` acceptable for convergence.

    maxiter, maxfev int

      Maximum allowed number of iterations and function evaluations. Will default to `N*1000`, 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.

    direc ndarray

      Initial set of direction vectors for the Powell method.

    return_all bool, optional

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

    bounds `Bounds`

      If bounds are not provided, then an unbounded line search will be used. If bounds are provided and the initial guess is within the bounds, then every function evaluation throughout the minimization procedure will be within the bounds. If bounds are provided, the initial guess is outside the bounds, and direc is full rank (or left to default), then some function evaluations during the first iteration may be outside the bounds, but every function evaluation after the first iteration will be within the bounds. If direc is not full rank, then some parameters may not be optimized and the solution is not guaranteed to be within the bounds.

    return_all bool, optional

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

minimize(method=’Nelder-Mead’) minimize(method=’CG’)