- 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).
For documentation for the rest of the parameters, see
Precision goal for the value of f in the stopping criterion.
Step size used for numerical approximation of the Jacobian.
Set to True to print convergence messages. If False, verbosity is ignored and set to 0.
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.