# scipy.optimize.fmin_powell

scipy.optimize.fmin_powell(func, x0, args=(), xtol=0.0001, ftol=0.0001, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0, callback=None, direc=None)

  Minimize a function using modified Powell’s method.

  This method only uses function values, not derivatives.

  Parameters
    func callable f(x,*args)

      Objective function to be minimized.

    x0 ndarray

      Initial guess.

    args tuple, optional

      Extra arguments passed to func.

    xtol float, optional

      Line-search error tolerance.

    ftol float, optional

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

    maxiter int, optional

      Maximum number of iterations to perform.

    maxfun int, optional

      Maximum number of function evaluations to make.

    full_output bool, optional

      If True, `fopt`, `xi`, `direc`, `iter`, `funcalls`, and `warnflag` are returned.

    disp bool, optional

      If True, print convergence messages.

    retall bool, optional

      If True, return a list of the solution at each iteration.

    callback callable, optional

      An optional user-supplied function, called after each iteration. Called as `callback(xk)`, where `xk` is the current parameter vector.

    direc ndarray, optional

      Initial fitting step and parameter order set as an (N, N) array, where N is the number of fitting parameters in x0. Defaults to step size 1.0 fitting all parameters simultaneously (`np.eye((N, N))`). To prevent initial consideration of values in a step or to change initial step size, set to 0 or desired step size in the Jth position in the Mth block, where J is the position in x0 and M is the desired evaluation step, with steps being evaluated in index order. Step size and ordering will change freely as minimization proceeds.

  Returns
    xopt ndarray

      Parameter which minimizes func.

    fopt number

      Value of function at minimum: `fopt = func(xopt)`.

    direc ndarray

      Current direction set.

    iter int

      Number of iterations.

    funcalls int

      Number of function calls made.

    warnflag int
      Integer warning flag:

        1 : Maximum number of function evaluations. 2 : Maximum number of iterations. 3 : NaN result encountered. 4 : The result is out of the provided bounds.

    allvecs list

      List of solutions at each iteration.

  See also

  `minimize`

    Interface to unconstrained minimization algorithms for multivariate functions. See the ‘Powell’ method in particular.

  Notes

  Uses a modification of Powell’s method to find the minimum of a function of N variables. Powell’s method is a conjugate direction method.

  The algorithm has two loops. The outer loop merely iterates over the inner loop. The inner loop minimizes over each current direction in the direction set. At the end of the inner loop, if certain conditions are met, the direction that gave the largest decrease is dropped and replaced with the difference between the current estimated x and the estimated x from the beginning of the inner-loop.

  The technical conditions for replacing the direction of greatest increase amount to checking that

  1. No further gain can be made along the direction of greatest increase from that iteration.

  2. The direction of greatest increase accounted for a large sufficient fraction of the decrease in the function value from that iteration of the inner loop.

  References

  Powell M.J.D. (1964) An efficient method for finding the minimum of a function of several variables without calculating derivatives, Computer Journal, 7 (2):155-162.

  Press W., Teukolsky S.A., Vetterling W.T., and Flannery B.P.: Numerical Recipes (any edition), Cambridge University Press

  Examples

      >>> def f(x):
      ...     return x**2

      >>> from scipy import optimize

      >>> minimum = optimize.fmin_powell(f, -1)
      Optimization terminated successfully.
               Current function value: 0.000000
               Iterations: 2
               Function evaluations: 18
      >>> minimum
      array(0.0)

scipy.optimize.fmin scipy.optimize.fmin_cg