minimize(method=’Newton-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 Newton-CG algorithm. - Note that the jac parameter (Jacobian) is required. - See also - For documentation for the rest of the parameters, see - scipy.optimize.minimize- Options:
- ——-
- dispbool
- Set to True to print convergence messages. 
- xtolfloat
- Average relative error in solution xopt acceptable for convergence. 
- maxiterint
- Maximum number of iterations to perform. 
- epsfloat or ndarray
- If hessp is approximated, use this value for the step size. 
- return_allbool, optional
- Set to True to return a list of the best solution at each of the iterations. 
- c1float, default: 1e-4
- Parameter for Armijo condition rule. 
- c2float, default: 0.9
- Parameter for curvature condition rule. 
 
 - Notes - Parameters c1 and c2 must satisfy - 0 < c1 < c2 < 1.