scipy.optimize.OptimizeResult

class scipy.optimize.OptimizeResult

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

__getitem__	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()	2-tuple; 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()