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__
($self, /)Return len(self).
clear
()copy
()fromkeys
($type, iterable[, value])Returns a new dict with keys from iterable and values equal to value.
get
(k[,d])items
()keys
()pop
(k[,d])If key is not found, d is returned if given, otherwise KeyError is raised
popitem
()2tuple; but raise KeyError if D is empty.
setdefault
(k[,d])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
()