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# scipy.optimize.fmin_cg

`scipy.optimize.``fmin_cg`(f, x0, fprime=None, args=(), gtol=1.0000000000000001e-05, norm=inf, epsilon=1.4901161193847656e-08, maxiter=None, full_output=0, disp=1, retall=0, callback=None)

  Minimize a function using a nonlinear conjugate gradient algorithm.

  Parameters: | f : callable f(x,*args) Objective function to be minimized. x0 : ndarray Initial guess. fprime : callable f’(x,*args) Function which computes the gradient of f. args : tuple Extra arguments passed to f and fprime. gtol : float Stop when norm of gradient is less than gtol. norm : float Order of vector norm to use. -Inf is min, Inf is max. epsilon : float or ndarray If fprime is approximated, use this value for the step size (can be scalar or vector). callback : callable An optional user-supplied function, called after each iteration. Called as callback(xk), where xk is the current parameter vector.
  Returns: | (xopt, {fopt, func_calls, grad_calls, warnflag}, {allvecs}) xopt : ndarray Parameters which minimize f, i.e. f(xopt) == fopt. fopt : float Minimum value found, f(xopt). func_calls : int The number of function_calls made. grad_calls : int The number of gradient calls made. warnflag : int 1 : Maximum number of iterations exceeded. 2 : Gradient and/or function calls not changing. allvecs : ndarray If retall is True (see other parameters below), then this vector containing the result at each iteration is returned.

  Other Parameters:
    maxiter : int
      Maximum number of iterations to perform.
    full_output : bool
      If True then return fopt, func_calls, grad_calls, and warnflag in addition to xopt.
    disp : bool
      Print convergence message if True.
    retall : bool
      return a list of results at each iteration if True.

  Notes: | Optimize the function, f, whose gradient is given by fprime using the nonlinear conjugate gradient algorithm of Polak and Ribiere See Wright, and Nocedal ‘Numerical Optimization’, 1999, pg. 120-122.

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