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# scipy.optimize.fmin_cg

`scipy.optimize.``fmin_cg`(f, x0, fprime=None, args=(), gtol=1e-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. Here x must be a 1-D array of the variables that are to be changed in the search for a minimum, and `args` are the other (fixed) parameters of f. x0 : ndarray A user-supplied initial estimate of xopt, the optimal value of x. It must be a 1-D array of values. fprime : callable, `fprime(x, *args)`, optional A function that returns the gradient of f at x. Here x and `args` are as described above for f. The returned value must be a 1-D array. Defaults to None, in which case the gradient is approximated numerically (see epsilon, below). args : tuple, optional Parameter values passed to f and fprime. Must be supplied whenever additional fixed parameters are needed to completely specify the functions f and fprime. gtol : float, optional Stop when the norm of the gradient is less than gtol. norm : float, optional Order to use for the norm of the gradient (`-np.Inf` is min, `np.Inf` is max). epsilon : float or ndarray, optional Step size(s) to use when fprime is approximated numerically. Can be a scalar or a 1-D array. Defaults to `sqrt(eps)`, with eps the floating point machine precision. Usually `sqrt(eps)` is about 1.5e-8. maxiter : int, optional Maximum number of iterations to perform. Default is `200 * len(x0)`. full_output : bool, optional If True, return fopt, func_calls, grad_calls, and warnflag in addition to xopt. See the Returns section below for additional information on optional return values. disp : bool, optional If True, return a convergence message, followed by xopt. retall : bool, optional If True, add to the returned values the results of each iteration. callback : callable, optional An optional user-supplied function, called after each iteration. Called as `callback(xk)`, where `xk` is the current value of x0.
  Returns: | xopt : ndarray Parameters which minimize f, i.e. `f(xopt) == fopt`. fopt : float, optional Minimum value found, f(xopt). Only returned if full_output is True. func_calls : int, optional The number of function_calls made. Only returned if full_output is True. grad_calls : int, optional The number of gradient calls made. Only returned if full_output is True. warnflag : int, optional Integer value with warning status, only returned if full_output is True. 0 : Success. 1 : The maximum number of iterations was exceeded. 2 : Gradient and/or function calls were not changing. May indicate that precision was lost, i.e., the routine did not converge. allvecs : list of ndarray, optional List of arrays, containing the results at each iteration. Only returned if retall is True.

  See also

  `minimize`
    common interface to all `scipy.optimize` algorithms for unconstrained and constrained minimization of multivariate functions. It provides an alternative way to call `fmin_cg`, by specifying `method='CG'`.

  Notes

  This conjugate gradient algorithm is based on that of Polak and Ribiere [R170].

  Conjugate gradient methods tend to work better when:

  1. f has a unique global minimizing point, and no local minima or other stationary points,
  2. f is, at least locally, reasonably well approximated by a quadratic function of the variables,
  3. f is continuous and has a continuous gradient,
  4. fprime is not too large, e.g., has a norm less than 1000,
  5. The initial guess, x0, is reasonably close to f ‘s global minimizing point, xopt.

  References

  [R170] | (1, 2) Wright & Nocedal, “Numerical Optimization”, 1999, pp. 120-122.

  Examples

  Example 1: seek the minimum value of the expression `a*u**2 + b*u*v + c*v**2 + d*u + e*v + f` for given values of the parameters and an initial guess `(u, v) = (0, 0)`.

      >>> args = (2, 3, 7, 8, 9, 10)  # parameter values
      >>> def f(x, *args):
      ...     u, v = x
      ...     a, b, c, d, e, f = args
      ...     return a*u**2 + b*u*v + c*v**2 + d*u + e*v + f
      >>> def gradf(x, *args):
      ...     u, v = x
      ...     a, b, c, d, e, f = args
      ...     gu = 2*a*u + b*v + d     # u-component of the gradient
      ...     gv = b*u + 2*c*v + e     # v-component of the gradient
      ...     return np.asarray((gu, gv))
      >>> x0 = np.asarray((0, 0))  # Initial guess.
      >>> from scipy import optimize
      >>> res1 = optimize.fmin_cg(f, x0, fprime=gradf, args=args)
      Optimization terminated successfully.
               Current function value: 1.617021
               Iterations: 4
               Function evaluations: 8
               Gradient evaluations: 8
      >>> res1
      array([-1.80851064, -0.25531915])

  Example 2: solve the same problem using the `minimize` function. (This myopts dictionary shows all of the available options, although in practice only non-default values would be needed. The returned value will be a dictionary.)

      >>> opts = {'maxiter' : None,    # default value.
      ...         'disp' : True,    # non-default value.
      ...         'gtol' : 1e-5,    # default value.
      ...         'norm' : np.inf,  # default value.
      ...         'eps' : 1.4901161193847656e-08}  # default value.
      >>> res2 = optimize.minimize(f, x0, jac=gradf, args=args,
      ...                          method='CG', options=opts)
      Optimization terminated successfully.
              Current function value: 1.617021
              Iterations: 4
              Function evaluations: 8
              Gradient evaluations: 8
      >>> res2.x  # minimum found
      array([-1.80851064, -0.25531915])