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, c1=0.0001, c2=0.4)[source]#

Minimize a function using a nonlinear conjugate gradient algorithm.

fcallable, 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.


A user-supplied initial estimate of xopt, the optimal value of x. It must be a 1-D array of values.

fprimecallable, 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).

argstuple, 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.

gtolfloat, optional

Stop when the norm of the gradient is less than gtol.

normfloat, optional

Order to use for the norm of the gradient (-np.inf is min, np.inf is max).

epsilonfloat 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.

maxiterint, optional

Maximum number of iterations to perform. Default is 200 * len(x0).

full_outputbool, 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.

dispbool, optional

If True, return a convergence message, followed by xopt.

retallbool, optional

If True, add to the returned values the results of each iteration.

callbackcallable, optional

An optional user-supplied function, called after each iteration. Called as callback(xk), where xk is the current value of x0.

c1float, default: 1e-4

Parameter for Armijo condition rule.

c2float, default: 0.4

Parameter for curvature condition rule.


Parameters which minimize f, i.e., f(xopt) == fopt.

foptfloat, optional

Minimum value found, f(xopt). Only returned if full_output is True.

func_callsint, optional

The number of function_calls made. Only returned if full_output is True.

grad_callsint, optional

The number of gradient calls made. Only returned if full_output is True.

warnflagint, optional

Integer value with warning status, only returned if full_output is True.

0 : Success.

1 : The maximum number of iterations was exceeded.

2Gradient and/or function calls were not changing. May indicate

that precision was lost, i.e., the routine did not converge.

3 : NaN result encountered.

allvecslist of ndarray, optional

List of arrays, containing the results at each iteration. Only returned if retall is True.

See also


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'.


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.

Parameters c1 and c2 must satisfy 0 < c1 < c2 < 1.



Wright & Nocedal, “Numerical Optimization”, 1999, pp. 120-122.


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).

>>> import numpy as np
>>> 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])