# 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
    ftol float

      Precision goal for the value of f in the stopping criterion.

    eps float

      Step size used for numerical approximation of the Jacobian.

    disp bool

      Set to True to print convergence messages. If False, verbosity is ignored and set to 0.

    maxiter int

      Maximum number of iterations.

    finite_diff_rel_step None 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. For `method='3-point'` the sign of h is ignored. If None (default) then step is selected automatically.

minimize(method=’COBYLA’) minimize(method=’trust-constr’)