minimize(method=’SLSQP’)#
- scipy.optimize.minimize(fun, x0, args=(), method='SLSQP', jac=None, bounds=None, constraints=(), tol=None, callback=None, options={'func': None, 'maxiter': 100, 'ftol': 1e-06, 'iprint': 1, 'disp': False, 'eps': 1.4901161193847656e-08, 'finite_diff_rel_step': 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(x0) * max(1, abs(x0)), possibly adjusted to fit into the bounds. Formethod='3-point'the sign of h is ignored. If None (default) then step is selected automatically.