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# 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(() -> None.  Remove all items from D.) copy(() -> a shallow copy of D) fromkeys Returns a new dict with keys from iterable and values equal to value. get((k[,d]) -> D[k] if k in D, ...) items(...) keys(...) pop((k[,d]) -> v, ...) If key is not found, d is returned if given, otherwise KeyError is raised popitem(() -> (k, v), ...) 2-tuple; but raise KeyError if D is empty. setdefault((k[,d]) -> D.get(k,d), ...) update(([E, ...) If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] values(...)