scipy.optimize.OptimizeResult¶
-
class
scipy.optimize.OptimizeResult[source]¶ Represents the optimization result.
Notes
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.
- Attributes
- xndarray
The solution of the optimization.
- successbool
Whether or not the optimizer exited successfully.
- statusint
Termination status of the optimizer. Its value depends on the underlying solver. Refer to message for details.
- messagestr
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_invobject
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, nhevint
Number of evaluations of the objective functions and of its Jacobian and Hessian.
- nitint
Number of iterations performed by the optimizer.
- maxcvfloat
The maximum constraint violation.
Methods
x.__getitem__(y) <==> x[y]
__len__(/)Return len(self).
clear()copy()fromkeys(iterable[, value])Create a new dictionary with keys from iterable and values set to value.
get(key[, default])Return the value for key if key is in the dictionary, else default.
items()keys()pop(k[,d])If key is not found, d is returned if given, otherwise KeyError is raised
popitem(/)Remove and return a (key, value) pair as a 2-tuple.
setdefault(key[, default])Insert key with a value of default if key is not in the dictionary.
update([E, ]**F)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]
values()