minimize(method=’SLSQP’)#
- scipy.optimize.minimize(fun, x0, args=(), method=None, jac=None, hess=None, hessp=None, bounds=None, constraints=(), tol=None, callback=None, options=None)
- Minimize a scalar function of one or more variables using Sequential Least Squares Programming (SLSQP). - See also - For documentation for the rest of the parameters, see - scipy.optimize.minimize- Options:
- ——-
- ftolfloat
- Precision goal for the value of f in the stopping criterion. 
- epsfloat
- Step size used for numerical approximation of the Jacobian. 
- dispbool
- Set to True to print convergence messages. If False, verbosity is ignored and set to 0. 
- maxiterint
- Maximum number of 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 jac. The absolute step size is computed as- h = rel_step * sign(x) * max(1, abs(x)), possibly adjusted to fit into the bounds. For- method='3-point'the sign of h is ignored. If None (default) then step is selected automatically.