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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)

  Minimize a function using the downhill simplex algorithm.

  Parameters: | func : callable func(x,*args) The objective function to be minimized. x0 : ndarray Initial guess. args : tuple Extra arguments passed to func, i.e. `f(x,*args)`. callback : callable Called after each iteration, as callback(xk), where xk is the current parameter vector.
  Returns: | (xopt, {fopt, iter, funcalls, warnflag}) 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.

  Other Parameters:

  > xtol : float
  >   Relative error in xopt acceptable for convergence.
  > ftol : number
  >   Relative error in func(xopt) acceptable for convergence.
  > maxiter : int
  >   Maximum number of iterations to perform.
  > maxfun : number
  >   Maximum number of function evaluations to make.
  > full_output : bool
  >   Set to True if fval and warnflag outputs are desired.
  > disp : bool
  >   Set to True to print convergence messages.
  > retall : bool
  >   Set to True to return list of solutions at each iteration.

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

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