Represents the optimization result.
There may be additional attributes not listed above depending of the specific solver. Since this class is essentially a subclass of dict with attribute accessors, one can see which attributes are available using the keys() method.
- x : ndarray
The solution of the optimization.
- success : bool
Whether or not the optimizer exited successfully.
- status : int
Termination status of the optimizer. Its value depends on the underlying solver. Refer to message for details.
- message : str
Description of the cause of the termination.
- fun, jac, hess: ndarray
Values of objective function, its Jacobian and its Hessian (if available). The Hessians may be approximations, see the documentation of the function in question.
- hess_inv : object
Inverse of the objective function’s Hessian; may be an approximation. Not available for all solvers. The type of this attribute may be either np.ndarray or scipy.sparse.linalg.LinearOperator.
- nfev, njev, nhev : int
Number of evaluations of the objective functions and of its Jacobian and Hessian.
- nit : int
Number of iterations performed by the optimizer.
- maxcv : float
The maximum constraint violation.
fromkeys($type, iterable[, value])
Returns a new dict with keys from iterable and values equal to value.
If key is not found, d is returned if given, otherwise KeyError is raised
2-tuple; but raise KeyError if D is empty.
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]