OptimizeResult#
- class scipy.optimize.OptimizeResult[source]#
- Represents the optimization result. - Notes - Depending on the specific solver being used, - OptimizeResultmay not have all attributes listed here, and they may have additional attributes not listed here. Since this class is essentially a subclass of dict with attribute accessors, one can see which attributes are available using the- OptimizeResult.keysmethod.- 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. 
- funfloat
- Value of objective function at x. 
- jac, hessndarray
- Values of objective function’s Jacobian and its Hessian at x (if available). The Hessian may be an approximation, 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(key[, default])- If the key is not found, return the default if given; otherwise, raise a KeyError. - 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()