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


Set to True to print convergence messages.


Average relative error in solution xopt acceptable for convergence.


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.


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