# 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)[source]

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. 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 [1].

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

 [1] (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])


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