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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 : 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 fopt 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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