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scipy.optimize.

# fmin

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

  Minimize a function using the downhill simplex algorithm.

  This algorithm only uses function values, not derivatives or second derivatives.

  Parameters:
    func callable func(x,*args)

      The objective function to be minimized.

    x0 ndarray

      Initial guess.

    args tuple, optional

      Extra arguments passed to func, i.e., `f(x,*args)`.

    xtol float, optional

      Absolute error in xopt between iterations that is acceptable for convergence.

    ftol number, optional

      Absolute error in func(xopt) between iterations that is acceptable for convergence.

    maxiter int, optional

      Maximum number of iterations to perform.

    maxfun number, optional

      Maximum number of function evaluations to make.

    full_output bool, optional

      Set to True if fopt and warnflag outputs are desired.

    disp bool, optional

      Set to True to print convergence messages.

    retall bool, optional

      Set to True to return list of solutions at each iteration.

    callback callable, optional

      Called after each iteration, as callback(xk), where xk is the current parameter vector.

    initial_simplex array_like of shape (N + 1, N), optional

      Initial simplex. If given, overrides x0. `initial_simplex[j,:]` should contain the coordinates of the jth vertex of the `N+1` vertices in the simplex, where `N` is the dimension.

  Returns:
    xopt ndarray

      Parameter that minimizes function.

    fopt float

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

    iter int

      Number of iterations performed.

    funcalls int

      Number of function calls made.

    warnflag int

      1 : Maximum number of function evaluations made. 2 : Maximum number of iterations reached.

    allvecs list

      Solution at each iteration.

  See also

  `minimize`

    Interface to minimization algorithms for multivariate functions. See the ‘Nelder-Mead’ method in particular.

  Notes

  Uses a Nelder-Mead simplex algorithm to find the minimum of function of one or more variables.

  This algorithm has a long history of successful use in applications. But it will usually be slower than an algorithm that uses first or second derivative information. In practice, it can have poor performance in high-dimensional problems and is not robust to minimizing complicated functions. Additionally, there currently is no complete theory describing when the algorithm will successfully converge to the minimum, or how fast it will if it does. Both the ftol and xtol criteria must be met for convergence.

  References

  [1]

  Nelder, J.A. and Mead, R. (1965), “A simplex method for function minimization”, The Computer Journal, 7, pp. 308-313

  [2]

  Wright, M.H. (1996), “Direct Search Methods: Once Scorned, Now Respectable”, in Numerical Analysis 1995, Proceedings of the 1995 Dundee Biennial Conference in Numerical Analysis, D.F. Griffiths and G.A. Watson (Eds.), Addison Wesley Longman, Harlow, UK, pp. 191-208.

  Examples

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

      >>> from scipy import optimize

      >>> minimum = optimize.fmin(f, 1)
      Optimization terminated successfully.
               Current function value: 0.000000
               Iterations: 17
               Function evaluations: 34
      >>> minimum[0]
      -8.8817841970012523e-16