minimize(method=’CG’)#
- scipy.optimize.minimize(fun, x0, args=(), method=None, jac=None, hess=None, hessp=None, bounds=None, constraints=(), tol=None, callback=None, options=None)
- 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:
- ——-
- dispbool
- Set to True to print convergence messages. 
- maxiterint
- Maximum number of iterations to perform. 
- gtolfloat
- Gradient norm must be less than gtol before successful termination. 
- normfloat
- Order of norm (Inf is max, -Inf is min). 
- epsfloat or ndarray
- If jac is None the absolute step size used for numerical approximation of the jacobian via forward differences. 
- return_allbool, optional
- Set to True to return a list of the best solution at each of the iterations. 
- finite_diff_rel_stepNone or array_like, optional
- If - jac in ['2-point', '3-point', 'cs']the relative step size to use for numerical approximation of the jacobian. The absolute step size is computed as- h = rel_step * sign(x) * max(1, abs(x)), possibly adjusted to fit into the bounds. For- jac='3-point'the sign of h is ignored. If None (default) then step is selected automatically.
- c1float, default: 1e-4
- Parameter for Armijo condition rule. 
- c2float, default: 0.4
- Parameter for curvature condition rule. 
- workersint, map-like callable, optional
- A map-like callable, such as multiprocessing.Pool.map for evaluating any numerical differentiation in parallel. This evaluation is carried out as - workers(fun, iterable).- Added in version 1.16.0. 
 
 - Notes - Parameters c1 and c2 must satisfy - 0 < c1 < c2 < 1.