# 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: 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.

Methods

 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()

#### Previous topic

scipy.optimize.show_options

#### Next topic

scipy.optimize.OptimizeResult.clear