#### Previous topic

minimize(method=’Powell’)

#### Next topic

minimize(method=’BFGS’)

# minimize(method=’CG’)

`scipy.optimize.``minimize`(fun, x0, args=(), method='CG', jac=None, tol=None, callback=None, options={'disp': False, 'gtol': 1e-05, 'eps': 1.4901161193847656e-08, 'return_all': False, 'maxiter': None, 'norm': inf})

  Minimization of scalar function of one or more variables using the conjugate gradient algorithm.

  See also

  For documentation for the rest of the parameters, see `scipy.optimize.minimize`

  Options: | disp : bool Set to True to print convergence messages. maxiter : int Maximum number of iterations to perform. gtol : float Gradient norm must be less than gtol before successful termination. norm : float Order of norm (Inf is max, -Inf is min). eps : float or ndarray If jac is approximated, use this value for the step size.