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# 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

  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, hess_inv | (ndarray) Values of objective function, Jacobian, Hessian or its inverse (if available). The Hessians may be approximations, see the documentation of the function in question.
  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`(...) | v defaults to None.
  `get`((k[,d]) -> D[k] if k in D, ...) |
  `has_key`((k) -> True if D has a key k, else False) |
  `items`(() -> list of D’s (key, value) pairs, ...) |
  `iteritems`(() -> an iterator over the (key, ...) |
  `iterkeys`(() -> an iterator over the keys of D) |
  `itervalues`(...) |
  `keys`(() -> list of D’s 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 present and has a .keys() method, does: for k in E: D[k] = E[k]
  `values`(() -> list of D’s values) |
  `viewitems`(...) |
  `viewkeys`(...) |
  `viewvalues`(...) |