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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.

Notes

OptimizeResult may have additional attributes not listed here depending on the specific solver being used. Since this class is essentially a subclass of dict with attribute accessors, one can see which attributes are available using the :None:None:`OptimizeResult.keys` method.

Represents the optimization result.

Examples

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.optimize._differentialevolution.DifferentialEvolutionSolver.solve scipy.optimize._root._root_broyden1_doc scipy.optimize._optimize.fmin_cg scipy.optimize._basinhopping.basinhopping scipy.optimize._linprog.linprog scipy.optimize._linprog_simplex._solve_simplex scipy.optimize._minimize.minimize scipy.optimize._shgo.SHGO.minimize scipy.optimize._root.root scipy.spatial._qhull.HalfspaceIntersection scipy.optimize._differentialevolution.differential_evolution scipy.optimize._qap.quadratic_assignment scipy.optimize._optimize.brute scipy.optimize._minimize.minimize_scalar scipy.optimize._optimize.OptimizeResult scipy.optimize._dual_annealing.dual_annealing

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Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them


GitHub : /scipy/optimize/_optimize.py#84
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